Integrated System Health Management: Perspectives on Systems Engineering Techniques

By Jiuping Xu and Lei Xu

ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors.

This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering.

• Solves prognostic information selection and decision-level information fusion issues
• Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment
• Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems
• Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction

• Language: English
• Publisher: Academic Press
• Release date: May 18, 2017
• ISBN: 9780128132685

Jiuping Xu

Jiuping Xu was born in 1962. He is a Distinguished Professor of “Cheung Kong Scholars Program”, the “Ten Thousand Project” National Leading Scholar of China, the academic leader of State Key Laboratory and the Assistant Principal at Sichuan University. Prof. Xu has been appointed as Lifetime Academician of the International Academy for Systems and Cybernetic Sciences. He is currently the Editor-in-Chief of International Journal of Management Science and Engineering Management, and World Journal of Modelling and Simulation since 2006. He is also the Vice-President of the Systems Engineering Society of China. He has published more than 40 books in well recognized presses and over 500 academic papers in areas of uncertainty decision-making, systems engineering, etc. in peer-reviewed journals, such as IEEE Transactions on Cybernetics, Aerospace Science and Technology, IEEE Transaction on Fuzzy Systems, IEEE Transactions on Aerospace and Electronic Systems, Journal of Aerospace Engineering, Journal of Aerospace Information Systems, International Journal of Systems Science and so on.

Book preview

Integrated System Health Management

Perspectives on Systems Engineering Techniques

Jiuping Xu

Uncertainty Decision Making Laboratory, Sichuan University, Chengdu, China

Lei Xu

Management Department, Xihua University, Chengdu, China

Table of Contents

Cover image

Title page

Copyright

Acknowledgments

Chapter One. ISHM for Complex Systems

Abstract

1.1 Overall Integrated System Health Management

1.2 Systematic Review on ISHM

1.3 ISHM Systems Engineering Application Features

References

Chapter Two. Sensor System and Health Monitoring

Abstract

2.1 Health Monitoring and Data Acquisition

2.2 Sensor Selection for ISHM

2.3 Decentralized Health Monitoring Detection

References

Chapter Three. Information Fusion

Abstract

3.1 Information Fusion for ISHM

3.2 Distributed Fusion Parameter Extraction

3.3 Data Mining and Processing for Diagnostics

3.4 Monitoring Data-Based ISHM Algorithm

References

Chapter Four. Performance Evaluation

Abstract

4.1 Key Problem Statement

4.2 Successful Launch Assessment

4.3 Improved Assessment Model

References

Chapter Five. System Assessment

Abstract

5.1 Assessment Index System

5.2 Effectiveness and Condition Assessment

5.3 System Reliability Estimation

References

Chapter Six. Fault Diagnostics

Abstract

6.1 Fault Diagnostics for Complex Systems

6.2 Adaptive Fault Diagnostics

6.3 Diagnostics Under Uncertainty

6.4 Integrated Hierarchical Diagnostics

References

Chapter Seven. Failure Prognostics

Abstract

7.1 Failure Prognostics for Complex Systems

7.2 Remaining Useful Life Prediction

7.3 Health Condition Prediction

7.4 Prognostics for State of Health

References

Chapter Eight. Maintenance Decision Support

Abstract

8.1 Complex Systems Maintenance Theory

8.2 Replacement Maintenance Strategy Decision-Making

8.3 CBM-based Maintenance Timing Decision-Making

References

Chapter Nine. Affordability and Life-Cycle Costs Analysis

Abstract

9.1 The Value and Cost for System Life Cycle

9.2 Elements and Process of System Life-Cycle Cost

9.3 Systems Life-Cycle Cost Accounting Process

9.4 Life-Cycle Cost Application to Complex Systems

References

Index

Copyright

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Acknowledgments

This work is supported by a Program of National Natural Science Foundation of China under Grant Number 71401136 and China Postdoctoral Science Foundation under Grant Number 2014M552375. The authors are indebted to the editors for their professional and valuable work.

Chapter One

ISHM for Complex Systems

Abstract

Complex systems, such as aerospace vehicles, are precise physical practical engineering systems consisting of many interrelated subsystems. As the main problem with complex systems is ensuring that the system continues to run safely, there has been significant research devoted to the maintenance of complex systems to extend the life of normal operations. Although maintenance has generally been carried out under fault conditions for complex spacecraft systems, this does not prevent huge losses or catastrophic accidents. Complex systems such as spacecraft have large number of fuzzy and uncertain factors with the nonlinearity degree increasing with technological development; in other words, integrated system health management (ISHM) has become an increasingly important application complex problem and has received a great deal of attention by governments and researchers. This chapter will take an overview of the overall ISHM for complex systems, identify its key issues emerged from its development course, and discuss the related systems engineering application features.

Keywords

Integrated health management; systematic review; capability development; key issues

1.1 Overall Integrated System Health Management

Integrated system health management (ISHM), which is an integrated artificial intelligence and information test application, has evolved to include a management ability that can autonomously reconfigure and assign resources to ensure mission safety and efficiency. This section will take an overview of the development of ISHM system, analyze the characteristics of ISHM for complex system from perspectives on systems engineering techniques, as well as its advancements combined with typical architectures, technical promoters, implementations, and challenges.

1.1.1 ISHM advancements

1.1.1.1 The ISHM foundation

As space contains infinite resources, the knowledge and development of advanced space high-tech cutting-edge technologies are becoming important when measuring a country’s comprehensive national strength. In the 19th and 20th centuries, it was said that whoever controlled the ocean, controlled the continent; however, as resources have depleted on earth, it is now said that whoever controls space, controls the entire planet. With rapid industrial and technological development in aerospace engineering, space exploration has developed from near-Earth observations to deep-space exploration. ISHM systems have evolved to include a management ability that can autonomously reconfigure and assign resources to ensure mission safety and efficiency. However, for deep-space exploration missions, communication delays and outages mandate that most ISHM functions now performed by ground controllers need to be performed onboard the spacecraft using a combination of human and autonomous control. In 2011, NASA retired the 30-year-old space shuttle program and announced plans to design a new manned spacecraft to replace the space shuttle for deep-space exploration missions [1]. Commercial space companies such as SpaceX have achieved significant breakthroughs in rocket launch recycling and have even begun to plan a Mars exploration and settlement expedition [2]. The United States and Russia have discussed cooperation for the construction of nuclear powered spacecraft, and China’s first official space station is scheduled to launch and be able to dock with the Shenzhou series spacecraft by 2020. At the same time, the European Space Agency and Japan have accelerated the implementation of manned space flight and deep-space exploration. In this process, as space vehicles, including manned spacecraft, are going to become indispensable for future space exploration, space complex systems are going to be primary to the peaceful development, exploration and utilization of space and its resources [3]. With the increased focus on deep-space exploration, there is a higher demand for spacecraft autonomy and the development of reliable, secure efficient complex systems. Fig. 1.1 illustrates the growth in demand for autonomous spacecraft demand released by NASA.

Figure 1.1 Autonomous spacecraft demand growth.

Spacecraft complexity means that the many subcomponents need to work together to ensure reliability and safety. In these complex modular systems, errors can occur in individual modules, severely affecting the interaction between modules, and possibly causing further errors which could evolve into faults or critical failures. One of the biggest security challenges for today’s complex systems is finding or preventing system faults and failures before they cause system failure. Therefore, based on the complexity and high-risk nature of space missions, many specialists are needed to perform operational and maintenance tasks. However, complex systems such as the software and hardware in spacecraft modules are often difficult to detect and diagnose under existing technical conditions. Further, as these systems tend to slow down or behave differently during spaceflight, catastrophic accidents can occur if a problem appears [4]. From 1959 to the end of 1995, the United States and Russia (as the Soviet Union) carried out 249 manned space missions, in which a total of 166 faults occurred, and four of which resulted in serious manned space accidents causing all astronauts to die: Apollo 4A in January 1967, Union 1 in April 1967, Union 11 in June 1971, and Challenger in January 1986. In addition, in a short period of 1 month from April to May 1999, the launch of the Hercules 4B, Athena 2, and Delta 2 launch vehicles from the United States all failed, resulting in billions of dollars in losses. In February 1996, China’s Long March III B launch vehicle failed on its first mission after a ground explosion killed eight people and injured dozens, in July and October 1999, a Russian proton rocket twice failed to launch satellites, in November 1999 the Japanese H2 launch vehicle failed to launch, in February 2003, the United States Columbia space shuttle explosion occurred, resulting in seven astronaut deaths and direct economic losses of $1.2 billion, and in June 2015 and September 2016, SpaceX’s rocket Falcon 9 exploded on launch. These serious spacecraft accidents not only resulted in a significant increase in systematic diagnostics research, but also highlighted the necessity for the comprehensive monitoring and accurate assessment of spacecraft system health conditions, fault diagnostics, failure prognostics, and the provision of system health management (SHM) architecture that could guarantee astronaut safety and mission success [5].

Space flight plans are under increasing pressure to improve operational and maintenance efficiencies while reducing the risk of spacecraft flight and achieving safe and reliable mission completion. For deep-space spacecraft, the harsh operating environment, the inability to repair or replace malfunctioning equipment, and the increasing task cycles and complexities increase the risk of mission failure and remain as major challenges [6]. As traditional complex space system design has focused on minimizing security risks, and protecting astronauts, workers, people, and expensive equipment assets, aerospace complex systems have focused on the development of highly reliable components and maintenance techniques to maximize security requirements and avoid mission failures. Therefore, ISHM was introduced to provide quasi-real-time evaluations of the system condition, safety margins, and maintenance to address spacecraft system safety and maintenance requirements. NASA defines ISHM as the process, approaches, and techniques to prevent or minimize the effects of faults in the system’s design, analysis, manufacture, validation, and operation [7]; therefore, ISHM covers design and manufacturing as well as management and operations. As ISHM development is focused on security, it has evolved from a traditional time-based maintenance [8] system to a preventive maintenance and condition-based maintenance (CBM) [9] system, in which the preventive maintenance arranges the maintenance plan based on the complex system fault characteristics to ensure trouble-free operations and training or mission completion, and the CBM allows the system to evaluate its own health condition, apply prognostics, and manage the faults. ISHM optimizes the use of sensor-collected system data based on information fusion techniques, utilizes appropriate analytical algorithms to evaluate the system health condition, monitors the fault symptoms in advance, applies failure prognostics before the fault occurs [10], and combines corresponding health management decision-making to apply appropriate support measures to achieve system CBM [11]. ISHM protects system functional integrity and space mission security and is the basis of the autonomous spacecraft.

1.1.1.2 The ISHM concept

Health management evolved from health monitoring theory and failure prognostics on the basis of condition assessment and fault diagnostics [11]. NASA, Boeing, and others have proposed concepts such as airplane health management (AHM) [12], the health and usage monitoring system (HUMS) [13], integrated vehicle health management (IVHM) [14], prognostics and health management (PHM) [15], ISHM [16]. The European Union has also launched Technologies and Techniques for New Maintenance Concepts and vigorously conducted health management methods research enhancing on-line, real-time, integrated monitoring, and strengthening remaining useful life (RUL) forecasting and maintenance decision support based on health condition and reliability. In November 2005, NASA held the first International Forum on Integrated System Health Engineering and Management (ISHEM) [17] in Napa, California, USA, which made the key decision to clearly identify ISHEM as a discipline with ISHM being the development and integration of the various approaches and techniques. In the following, a brief review is given of the evolution of health management-related disciplines:

– In the 1950s and 1960s, reliability theory, environmental and system testing, and quality methods emerged.

– In the 1960s, redundancy management and fault-tolerant methods gained traction.

– In the 1970s and 1980s, Byzantine computer fault theory was developed.

– In the 1980s and early 1990s, total quality management was instigated.

– In the 1990s, new standards for integrated diagnostics and maintainability were agreed on.

– At the beginning of the 21st century, air vehicle and SHM was a prime focus for technical aerospace applications.

Conceptually, integrated SHM evolved from NASA’s vehicle health monitoring (VHM) in the 1980s and 1990s, which had been initially used to select the appropriate sensors and software to monitor the vehicle’s health. However, researchers found that monitoring did not reflect the behavior indicated by the data, so management replaced monitoring. Fig. 1.2 shows the evolution in ISHM-related concepts.

Figure 1.2 Evolution in ISHM-related concepts.

Significant research has focused on IVHM concepts [18–20], a concept that dates back to the 1970s [21], and NASA formally introduced their IVHM program as part of its reusable launch vehicle (RLV) program. In summary, IVHM refers to the assessment and prognostics of aerospace vehicles to enhance operational decision-making and improve operational efficiency and the corresponding economic benefits [22]. In addition to aircraft and spacecraft, some researchers have proposed similar IVHM functional systems, including helicopters, land vehicles (cars, trains, military vehicles), and maritime systems (ships and submarines) [23]. Research has also explored IVHM-related design approaches [24], technological developments and integration [20], logistics support [25,26], and development planning [27]. However, in the mid-1990s, SHM was developed as a more comprehensive standard term as the term vehicle involved only the design and operation of aerospace engineering complex systems. The US Department of Defense consequently proposed integrated diagnostics for the operation and maintenance of cutting-edge equipment, a term which was quickly incorporated into NASA terminology to deal with system-level fault-related issues across various disciplines, from which ISHM was developed [28].

ISHEM was more recently proposed by NASA as a new extension of ISHM to include design and manufacture and operational and management approaches [29]. ISHM covers such areas as advanced sensors, redundancy management, artificial intelligence diagnostics, probabilistic reliability theory, and verification methods, as well as quality control, system architecture and engineering, knowledge acquisition, and human–machine systems, thereby covering both existing and new issues.

For the design of the system’s health management capabilities, a new enhanced CBM plus (CBM+) has also been proposed recently to further unify reliability management, automatic support, and maintenance operations with the goal of systematically planning, designing and integrating condition monitoring, life prognostics, maintenance decision-making, logistics support, and cost control after the system is put into service. These new concepts, theories, and approaches provide new support for the design, manufacture, operations, maintenance, and security of complex systems.

1.1.1.3 Typical ISHM architecture

ISHM sensor and computer processes and activities are controlled by engineers in the air/space and on the ground. At present, most current vehicle ISHM functions are almost fully controlled by engineers, with the software handling only a limited set of functions. However, by increasing ISHM system functionality, software programs could support more autonomy for distant crews and enable the crew and ground support team to focus on the scientific and mission objectives. Because most complex systems are electromechanical integration systems; there are many similarities. As ISHM concepts have evolved, there has been increased research attention on universal ISHM architecture [30]. Three typical ISHM architectural formats are discussed in the followings [31]:

1. NASA IVHM Livingstone model-based reasoning engine

IVHM, which is a large research project being developed in cooperation by the Glenn Research Center (GRC), the Ames research center, Honeywell corporation, Boeing and MIT, was initiated by NASA to ensure vehicle crew safety and mission success. As vehicle architectural structure is similar, this project focuses on the development of a model-based reasoning diagnostics engine that has a universality to allow it to be the core of a complex system ISHM. Livingstone has been included as part of the Propulsion IVHM Technology Experiment (PITEX) and has been demonstrated on an X-34 main propulsion system. The original version of Livingstone was Lisp-based and the current version of Livingstone, called L2, can track multiple trajectories in the system over time. The IVHM architecture and L2 have been applied in the X-37, the Earth Observation One satellite and F/A-18 aircraft with great success [32]. The X-37 is taken as an example here to describe the specifics of the IVHM architecture.

The core of the X-37 IVHM is deployed in the Vehicle Management Computer (VMC), within which the Vehicle Management Software (VMS) and the Livingstone operate [33]. The VMS receives sensor information from the key system components, deals with the information, and then harnesses the Livingstone to complete the fault diagnostics and prognostics tasks; system models stored in the VMC are used for the reasoning, and the VMS also sends the processed information to the ground operator for additional processing. Because of the upgraded calculation and storage capacity of the onboard computer, this architecture allows most health management and system reliability data processing and reasoning to be done onboard, thus avoiding the need for mass space-ground data transmission. The Livingstone reasoning and prognostics module is the most important part of universal complex system ISHM, with the other complex ISHM being done by the VHM. The architectural design ensures that the VMS relies on the system; however, because of the complexity, there is a complicated intricate design process, meaning that seeking universality is difficult.

An ISHM test facility is to be developed to integrate the key technologies for demonstration, benchmarking, validation, and development purposes. The test facility is expected to address the intelligence needs for future spacecraft, autonomous systems, adaptive systems, and intuitive and highly networked engineering design environments. This test facility has also been designed to support space transportation and space systems in which advanced data networking, advanced vehicle intelligence, and the integration, verification, and validation of key technologies is paramount. Key systems that require IVHM are as follows:

a. Propulsion systems: main, auxiliary, and propellant feed systems;

b. Structural systems: sensing, analysis interpretation, and prognostics;

c. Thermal protection systems: avoid loss of crew, vehicle, or mission;

d. Power & actuators: power management and distribution, power sources (i.e., batteries, fuel cells, turbine power units);

e. Avionics: single bit problems, software and hardware anomalies, sensor and data validation, and communication systems.

2. PHM for the US air force Joint Strike Fighter

The F-35 Joint Strike Fighter is a new generation attack plane developed by Lockheed Martine Aeronautics Company [34]. The aim behind the PHM was to develop a next generation affordable attack plane weapons system with global support that economically diagnoses and prognoses faults based on sensor data, and then isolates these faults using layered intelligence reasoning software. It is estimated that the application of the PHM and other advanced technology can reduce the Can Not Duplicate rate by 82%, the maintenance manpower by 20%–40%, the logistics machinery footprint required to support the aircraft by 50%, and the use and maintenance costs by more than 50%, resulting in an increase in service life to 8000 flight hours and a 25% increase in sortie generation rates [35].

As shown in Fig. 1.3, the integrated PHM system has both onboard and off-board operations. The layered reasoning structure makes it easier to apply diagnostics and prognostics at the component, subsystem and overall system levels. The onboard PHM has three main operations. The first are the software and hardware component monitoring systems in each JSF component, the main functions for which are primary signal processing and built-in tests (BITs). The second operation processes and abstracts the original information and transmits it to the Area Managers who fuse and process the information and manage each key component individually to determine the current health condition.

Then, the third operation of the onboard PHM transmits the reasoned and abstracted health condition information to the Vehicle Manager to determine fault location and automatically perform operations to mitigate the possible effects. The information is fused to develop an intuitionistic knowledge base regarding the health condition of the entire system, which is then sent to a ground-based Automatic Logistic Information System that prognoses the health conditions using a more powerful calculation and analysis ability so as to complete further fault isolation (FI), prognostics, and advanced trace/prognostics on the health states and components, generate the subsequent flight and maintenance plans for the overall system, and give notice to the relevant ground department to arrange parts and maintenance. The Vehicle Manager and Area Managers are stored in the integrated core processor and so are able to analyze and give prognostics for key systems and components using model reasoning and advanced artificial intelligence technology. The three layers of reasoning are shown in Fig. 1.4.

This layered area management methodology has highly efficient data processing and is able to provide a systemic analysis of components and the entire system while isolating unreliable components and implementing reliable fault diagnostics and management. The data processing work is partitioned into different phases, and the work is finished using a different processor, thereby reducing the work of the single processor; however, as with the ISHM system, the onboard PHM is affected by the real-time complexity of the system, memory throughput, and processor resource usability.

3. Open system architecture CBM (OSA-CBM)

The limitations of traditional fault diagnostics and health management is that each given complex system requires a special health management architecture design as there is no ready-made standard data acquisition interface or interoperability and extendibility protocols. Faced with such limitations, the US Navy initiated research into an OSA-CBM [36], the system maintenance strategy development process for which had three steps: corrective early stage maintenance, preventive maintenance, and the current CBM. Fig. 1.5 shows the OSA-CBM system architecture [37].

The architecture has seven layers from sensor data processing through to decision support, which was adopted to capture the data and information transition from the sensor to the user. As each layer is a collection of similar tasks or functions at different abstraction levels, it is a relaxed layered framework. The main function of each layer is described below [7,38]:

a. Signal Processing processes inputs to the complex system in the form of sensor data to characterize the data content in accordance with a desired data format.

b. Condition Monitoring gathers the Signal Processing data and compares it to specific predefined features in the highest physical site specific application.

c. Health Assessment acquires the input data from the condition monitors or from other health assessment modules to determine the degradation levels in the monitored system, subsystem, or piece of equipment.

d. Prognostics generates the estimates RUL of a component or subsystem for given usage profiles and provides recommended maintenance or operational actions and alternatives as well as the implications of each recommendation.

e. Decision Support utilizes the spares, logistics, etc. to assemble maintenance options.

f. Presentation supports the information presentation to system users such as maintenance and operations personnel.

Figure 1.3 F-35 JSF PHM architecture.

Figure 1.4 Three-layered PHM reasoning structure.

Figure 1.5 OSA-CBM system architecture.

The OSA-CBM standard released today uses UML to describe data models, with the interfaces between layers being defined using XML. The outputs include any information produced by the lower layers, and the inputs include any information required by the lower layers. The OSA-CBM abstractly disassembles the SHM activities and identifies the ISHM functions to be conducted on each layer. As it is convenient for data exchange, the data model definition adopts XML to avoid compatibility problems when communicating the different data sources [39]. All parts of the OSA-CBM adopt middleware technology, which allow all modules to have a high degree of universalization regarding interface and data exchange standards. Applying OSA-CBM to the next generation health management technology has become a popular research area with applications spanning aviation, aerospace, marine complex systems, and heavy machinery and industrial processes.

1.1.2 ISHM capability development

Here, ISHM capability is defined as the ability to integrate data, information, and knowledge (DIaK) conceptually and physically distributed throughout the system elements; in other words, to effectively manage the DIaK associated with distributed subsystems [40]. The term DIaK management encompasses contextual and timely storage, distribution, sharing, maintenance, processing, reasoning, and presentation for any system element.

1.1.2.1 ISHM goal

The ISHM functional capability level (FCL) measures how well the system: (1) assesses health conditions, (2) diagnoses faults, (3) prognoses anomalies or failures, (4) enables the efficient integration and execution of the whole system life-cycle from systems engineering perspective, and (5) provides an integrated awareness regarding the condition of important system elements to support decision-making [5].

Rather than using the prognostics function, early complex system fault diagnostics techniques are emphasized for the fault representation monitoring. The ISHM includes key techniques such as traditional fault diagnostics, subsystem-level RUL prognostics, and intelligence decision support, with the goals of providing information about current health conditions for the complex system and crew, performing system health autonomic logistics, ensuring safe and complete missions, reducing maintenance costs, and improving overall system life cycle efficiency. A health management system that has all FCL functions is regarded as an ISHM system; however, because of differences in complex system structural developments, calculation and storage capacities, and the characteristics of the applied environment, ISHM architecture differs depending on the specific complex system.

1.1.2.2 ISHM system benefits

An ISHM system has two main benefits based on the functional and operational performance characteristics of the complex system related to the actual operational processes of the system: Mission Availability and Mission Capability [41].

1. Mission availability

Mission Availability encompasses all ISHM aspects to prepare the system for mission launch from in-flight fault diagnostics to maintenance action. It also includes conventional CBM and is geared toward ensuring the vehicle can perform its mission when assigned. These scenarios are heavily dependent on diagnostics, RUL estimation and automation.

2. Mission capability

Mission capability focuses on the potential of new capabilities such as using structure/antenna interaction to improve performance. In particular, it includes new approaches to the integration of different subsystems into a whole to generate new abilities and also addresses the development and exploitation of theater-wide ISHM-based planning and execution.

1.1.2.3 Major functional capabilities

ISHM techniques address abnormal system operational behavior, including both system health determination functions and effective health condition information [42,43]. The major functional capabilities [44,45] usually addressed by ISHM techniques are:

1. Condition assessment: Integrate the sensor system’s information from multiple subsystems to assess system health condition. Support deeper investigations at the desired level of detail.

2. Fault detection: Integrate information to identify the current condition and degradation, abnormal behavior and fault symptoms in the complex system, and its components.

3. Diagnostics: Integrate and analyze information about system state and symptoms from built-in fault detection, isolation, and recovery (FDIR) capabilities to determine and communicate the root cause of the detected problems.

4. Prognostics: Determine the possibility of and the time when conditions or trends could lead to faults or failures. Determine the optimal preventive maintenance time, and enable logistics and operations to better manage the overall system.

5. Performance evaluation: Identify the impact of anomalous conditions on system performance, such as lost or degraded redundancy, resources, and functionality. Combined with prognostics, analyze and determine the most likely subsequent failures.

6. Decision support: Propose and prioritize countermeasures for troubleshooting or maintenance actions. Provide ISHM information that is pertinent to decision-making and apply timing, resource, and other procedures.

1.1.2.4 Adjustable and collaborative autonomy

Adjustable autonomy allows the automation level for control mechanisms, modularity, and restart features to be adjusted according to mission or crew requirements. Levels can be adjusted from fully automated operations when human intervention is unnecessary or impossible to fully manual operations where operators vigilantly monitor and control. To better maintain system condition assessment and configuration, crews generally prefer to perform the health management activities; however, over time in deep-space exploration, crews may tire of routine work and use ISHM automation to allow them to spend more time on exploration tasks. ISHM assessment makes it easy for the human operators to determine the operational system state and control and adjust the level of autonomy or operational state with minimal effort [46].

Collaborative autonomous systems allow automated systems to function more like team members as they have hybrid initiative capabilities that facilitate give-and-take collaboration and use volunteered information and bidirectional communication to support incremental understanding and problem-solving. In a well-designed ISHM system, human–computer interactions are minimized, and the time between necessary human–computer interactions is maximized.

1.1.2.5 ISHM standards

ISHM capability development requires the use of information and data models that are associated with the various system elements. The term model is used in the broadest sense here as it can include qualitative, analytic, statistical, artificial intelligence, and other model types. Model use is enabled by the DIaK management, and encompasses storage, distribution, sharing, maintenance, processing, reasoning, and presentation; therefore, standards must be established to allow the DIaK to economically operate in a plug & play and interoperable environment.

ISHM standards must be at a high-enough layer in the infrastructure to be largely independent of the physical and transmission layers such as TCP/IP [10]. Example standards for ISHM include the IEEE 1451 family of standards for smart sensors and actuators, the OSA-CBM standard, and the Open systems Architecture for Enterprise standard managed by the Machine Information Management Open Standards Alliance. These standards are sufficiently abstracted so that they can be implemented as part of any complex system architecture [47].

1.1.3 Technical ISHM enablers

1.1.3.1 Technical promoters

1. Sensors and sampling

The instrumentation provides human operators and the software a better insight into error correction for the systems, subsystems, and module health of possible redundant sensors. Technological sensor advances have led to the production of smaller, lighter, self-calibrating, and self-testing sensors that consume minimal power. More fully instrumented spacecraft systems enable engineers to validate models and identify the modifications needed to increase reliability. Vehicle data transmission and storage capacities and adequate sampling rates and bandwidth are also important ISHM enablers.

The correct number of sensors in the right positions simplifies diagnostics and reduces ISHM system complexity; therefore, an optimal design goal would be a minimum number of sensors placed in appropriate locations so that each identified critical failure mode or combination has a unique failure signature. This capability has to be able to intervene in interplanetary missions to provide timely assistance for troubleshooting when communication delays and blackouts hamper ground control.

2. Updating and evolving information architecture

Adaptation, updating, and ISHM system evolution are vital for aerospace missions to allow for (1) the discovery of novel system health problems, (2) the migration of ISHM functions from ground to onboard during a long mission, (3) automation level adjustments during a mission or operation process, and (4) technological improvements during a multimission period. As aerospace exploration missions become more remote, ISHM capabilities on earth need to migrate from being ground-based to operating as autonomous onboard systems. Likewise, as ISHM technologies mature and missions become more remote, systems management capabilities need to be upgraded.

Space Shuttle architecture and systems have proven to be inflexible, making the development and integration of ISHM technologies into the vehicles prohibitively expensive [22]. The information and data architecture in future spacecraft must be designed to support ISHM models, data adaptation and updating, and advanced upgrades, and the architecture must also be able to accommodate adjustable autonomy levels during and/or between missions.

1.1.3.2 Engineering drivers

1. ISHM systems engineering

As models and sensors are critical to ISHM success, processes and methods are required that allow subsystem and component designers to work with ISHM software designers to determine the number, type, and location of sensors, with a focus on detectability and definitive diagnoses. One of the main ISHM drivers is a knowledge of design, interaction, and performance (design data and models), and so ISHM systems engineering includes acquiring and accessing that knowledge. There is a need for processes and techniques to capture, organize, and mine the knowledge used for ISHM system models.

2. Assessment tools

Allocating ISHM functions between human operators and system operators must be performed to maximize ISHM technical investment. Johnson Space Center has developed a Function-specific Level of Autonomy and Automation Tool (FLOAAT) [48] that uses questionnaires to assess the autonomy of each functional capacity and the consistency of the automated level, from which an analytical results summary is generated. The FLOAAT tool has been used to generate the independent autonomous, automation-related Exploration Level 2 architectural requirements for Orion’s Rendezvous, Proximity Operation and Docking system [49]. This Delphi-like rating technique could also be useful for ISHM autonomous allocations. At present, the tool has been designed for fixed allocations but needs to be further modified to account for adjustable and variable distribution autonomy.

3. Test capabilities

One difficulty that ISHM technological developers face is the lack of testing environments relevant to the technological maturity level. For low-mature technologies, a test environment that operates on a low-cost desktop computer may be sufficient; however, for realistic test environments that have increasing fidelity, technologies such as PITEX simulations are needed to ready the technologies for operations. For operational testing, environments such as terrestrial analog sites or even space-based assets can provide low-cost alternatives to expensive dedicated test missions. The lack of appropriate and accessible test facilities increases the risk that the technology cannot be prepared in time.

1.1.4 ISHM challenges

ISHM engineers need to continuously research and develop capabilities to enhance ISHM systems. However, as most technical development drives technical readiness in engineering and integration, engineers are less likely to be aware of these needs. Although ISHM techniques have been applied to complex systems such as aviation and aerospace vehicles, there has been limited operational experience in aerospace programs. The overall impression of NASA engineers is that ISHM is too risky [21]. This section describes the sources of these perceptions and explores the ISHM developmental and implementation challenges.

1.1.4.1 Capabilities, sensors, and data

Much ISHM technological development has focused on model-based diagnostics; however, additional work is required to integrate many technological approaches and information sources. An integrated approach should be able to diagnose additional complex fault situations so as to improve model expression and engine capability; further, additional strategies and functions are needed to control interactions with the embedded system, for fault detection (FD), identification and reconfiguration (FDIR), and for caution and warning. ISHM technical complexity is considered a major risk; however, the separation of the ISHM system from the models and data reduces complexity and increases reusability, comprehensibility, and system maintenance ease of use. Therefore, work is needed to further reduce the inherent complexities, and technical development is also needed in some neglected high-value areas such as condition assessments, performance evaluations, and what if scenario evaluations, including the next worst case failure analyses.

As human–machine systems face the same limitations when needed information is not available, advances in sensors, data storage, and communications are needed to support increased sampling rates and bandwidth as any ISHM system (automatic or manual) needs sufficient high-quality data to operate effectively.

1.1.4.2 ISHM engineering advances

Because advanced mature systems are concerned with engineering, integration approaches, and tools, there is a need for an intermediate developmental stage to reduce risk, in which techniques are redeveloped to utilize the engineering processes and procedures to develop, implement, test, and certify critical systems.

1. Processes and operations

As ISHM techniques must be more secure and more reliable than managed systems, the design and evaluation of reliable ISHM systems needs to be accelerated. Technological demonstrations should include rigorous development plans and demonstrate supportable, scalable, and upgradeable technologies with low operational costs. Technologies are poor if an engineer is unable to demonstrate the use of the processes necessary to keep an application functioning during operations, or when dated information is used to construct the models and applications, even if this is the best information available. To gain credibility, technological demonstrations must upgrade data, models, and applications using current data and operational change procedures. Using a change process, engineers can also evaluate the cost and schedule the impact of the changes that affect ISHM applications.

2. Model capture and update

As modeling costs are a general problem for aerospace programs, processes and methods are required to facilitate model reuse throughout a system life cycle and to reduce the risks of developing duplicate models for multiple organizations. Because ISHM development could benefit from model capture and reuse improvements, engineers should be encouraged to design scalable and upgradeable ISHM system models and approaches as configurations change.

3. Testing

ISHM requires adequate and updated test benches and test facilities to provide the mission architecture and data necessary to enable testing in an environment close to the intended operating environment. An extensible test system is therefore needed to provide spacecraft telemetry for testing and verification. Advanced model-based ISHM systems need to be tested in nondestructive virtual environments that challenge the data from processor failures, environmental faults, complex fault concatenations, and errors or uncertainties. Using a ground simulation station as a test bench could assist in help validating ISHM systems over long-term testing.

1.1.4.3 Program organization and infrastructure

1. Compatible mission and infrastructure

To successfully integrate ISHM into future spacecraft, the designs must accommodate complex system software and hardware integration and evolution. The systems, infrastructure, and processes must be designed so that the models and data used by the system can be updated periodically as new and unusual conditions are analyzed and understood.

2. Cross-organizational support

As ISHM systems can aid and automate the complex assessment functions currently being performed by distributed mission personnel in space and on Earth, programs need to address the organizational complexity that makes managing cross-organizational systems difficult. These difficulties have also contributed to the cost and difficulties in ISHM technological maturation; therefore, further work is needed on approaches and techniques that maximize the combined performance of the complex human–machine system across organizational boundaries, including the ability to adapt to autonomy, collaboration, information fusion, and condition assessment for information presentation.

1.2 Systematic Review on ISHM

Overall, the ISHM system mainly utilizes integration of advanced sensors and diagnostics, prognostics and managements the system health with various algorithms and intelligent models. This section made a systematic review on ISHM to identify the key issues of an ISHM system from perspectives on system engineering techniques.

1.2.1 Specify research issues

ISHM-related theories and techniques research have different research purpose according to different application objects. Take the spacecraft as an example, the ISHM application objects can be divided into three categories, namely the operation class, general class, and engineering class [50]. As shown in Fig. 1.6, the operation class includes applications for support, flight, serving, vehicle management and training; the general class includes condition assessment, fault diagnostics, failure prognostics, and maintenance decision; the engineering class includes supply, R&D, and manufacturing.

Figure 1.6 Application objectives of ISHM.

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