1、Integrated Vehicle Health Management - System of Systems IntegrationOther SAE Books of Interest: No Fault Found: The Search for the Root Cause By Samir Khan, Paul Phillips, Chris Hockley, Ian K. Jennions (Product Code: R-441) Integrated Vehicle Health Management: Perspectives on an Emerging Field By
2、 Ian K. Jennions (Product Code: R-405) Integrated Vehicle Health Management: Implementation and Lessons Learned Ian K. Jennions (Product Code: R-438) For more information or to order a book, contact: SAE INTERNATIONAL 400 Commonwealth Drive Warrendale, PA 15096 Phone: +1.877.606.7323 (U.S. and Canad
3、a only) or +1.724.776.4970 (outside U.S. and Canada) Fax: +1.724.776.0790 Email: CustomerServicesae.org Website: books.sae.orgIntegrated Vehicle Health Management - System of Systems Integration Edited by Timothy Wilmering Warrendale, Pennsylvania, USACopyright 2017 SAE International. All rights res
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9、e.org 400 Commonwealth Drive Warrendale, PA 15096 E-mail: CustomerServicesae.org Phone: +1.877.606.7323 (inside USA and Canada)+1.724.776.4970 (outside USA) Fax: +1.724.776.0790v Table of Contents Introduction . vii Design Features of the 747-400 Electric Power System (892227) . 1 Jim Thom and John
10、Flick, Sundstrand Corporation Diagnosis Concept for Future Vehicle Electronic Systems (2004-21-0010) . 13 Gary Abusamra, Rob Carpenter, and Stephen Kiselewich, Delphi Corporation Hierarchical Component-based Fault Diagnostics for By-Wire Systems (2004-01-0285) 23 Song You and Laci Jalics, Delphi Cor
11、poration Vehicle Level Approach for Optimization of On-Board Diagnostic Strategies for Fault Management (2013-01-0957) 31 Dibyendu Palai, Tata Motors Ltd. A Hierarchical Reasoning Structure to Support Aerospace IVHM (2011-01-2665) . 47 Michael Roemer, Impact Technologies LLC Solid State Power Contro
12、l as a Network Backbone for Aircraft System Health Management (2012-01-2233) . 55 Michael Ballas and Fred Potter, Astronics Corp. Integration Issues for Vehicle Level Distributed Diagnostic Reasoners (2013-01-2294) . 65 Faisal Khan, Cranfield University; Ian Jennions, IVHM Centre Cranfield Universit
13、y; and Tarapong Sreenuch, Cranfield University Design and Evaluation of Plug-and-Play Enabled IVHM Architecture (2015-01-9001) 71 Tarapong Sreenuch and Ian Jennions, IVHM Centre Cranfield University Health Ready Components-Unlocking the Potential of IVHM (2016-01-0075) . 85 Steven Holland, General M
14、otors Company; Tim Felke, Honeywell Aerospace; Luis Hernandez, Global Strategic Solutions LLC; Robab Safa-Bakhsh, Boeing Research and Matthew A. Wuensch, Honeywellvi IVHM Development and the Big Data Paradigm (2013-01-2332) 93 Lucas Campos Puttini, Bombardier Aerospace About the Editor 103vii Integr
15、ated Vehicle Health Management - System of Systems Integration 1.0 Introduction Owners and operators of complex engineered systems assume economic and safety imperatives associated with the ever-increasing complexity of systems. This is particularly true in the domain of transportation systems, such
16、 as automotive and aerospace, and places strong demands on the designers and maintainers of those systems to minimize critical performance issues and downtime. Integrated Vehicle Health Management (IVHM) is a multifaceted discipline that has emerged to meet the technical challenges in effecting thos
17、e goals, which requires the ability to: 1. Accurately predict the time to failure or remaining useful life of critical components or subsystems and 2. Effectively isolate the root cause of failures once their effects have been observed and 3. Provide system stakeholders actionable information in sup
18、port of operations, maintenance and support. IVHM, then, can be thought of as the unified capability of a system of systems to assess the current or future state of the member system health, and integrate that picture of system health within a framework of available resources and operational demand.
19、 This requires effective capabilities to assess applicable fault detection, diagnostic, prognostic and reasoning technologies. The goal of the assessment is to optimize the systems ability to detect and isolate subsystem failures, predict the remaining useful life of the faulty component, and assess
20、 decision support and reasoning capabilities. A fully integrated approach will then map failure events and consequent effects across subsystem boundaries and system levels of indenture to preventive or mitigating maintenance approaches to restoring system functionat minimal cost and safety impact. 2
21、.0 Background The SAE Health Management-1 (HM-1) Technical Group organized and held their first meeting in April 2011. HM-1 is in some ways a logical extension of long-existing work by several groups within SAE International (www.sae.org) that deal with aspects of system health management, including
22、: S-18: Aircraft and Systems Development and Safety Assessment E-32: Aerospace Propulsion Systems Health Management G-11: Reliability, Maintainability/Supportability and Probabilistic Methods Group G-11 SHM: Structural Health Monitoring and Management S-12: Helicopter Powerplant AS-3: Fiber Optics a
23、nd Applied Photonics A-6: Aerospace Actuation, Control and Fluid Power Systems Steering Group AE-5: Aerospace Fuel, Oil and Oxidizer Systems Steering Group The SAE HM-1 Committee serves as a forum to gather, develop, record and publish expert information in the discipline of aerospace Integrated Veh
24、icle Health Management. It addresses the integration of health management systems at both the platform and fleet levels, and provides technical standardization to assist, guide and advance the realization of IVHM. Ten papers have been selected from a review of recent SAE technical paperswe discuss t
25、he contribution each has made to exploring and advancing important concepts of IVHM integration in complex aerospace and automotive systems. 3.0 Definition and Scope As system complexities have increased, there has been a corresponding rise in system support costs. This is driven in large part by mo
26、re frequent and often enigmatic subsystem failures that can be obscured by interactions of coupled subsystems in todays complex System of Systems (SoS) architectures. Traditional Built in Test (BIT) approaches typically monitor subsystem behavior and report local failures, but do a poor job of detec
27、ting or isolating failures across subsystem boundaries due to the effects of subsystem interactions, such as: fault and/or symptom cascades, inhibitions of subsystem faults or symptoms due to operation modes and conditions, and propagation of system level symptoms, effects, faults and compounded sys
28、tem degradation. IVHM strategies can be used to mitigate these issues by taking an SoS view reflecting the highly-integrated nature of complex systems, providing more comprehensive failure detection and prediction capabilities. Combined with advanced decision support methods, these approaches can be
29、 used to more effectively predict, isolate, schedule, and repair failed subsystems, reducing platform support costs and minimizing platform downtime. The implementation of IVHM at the platform level therefore requires a carefully thought-out approach that assesses the nature and interaction of the s
30、ubsystems to be addressed, and creates a strategy that incorporates data acquisition, multi-level processing, and communications that is incorporated into the SoS design. These elements are necessarily highly optimized to take best advantage of the constrained resources available. This is best accom
31、plished in a clean-sheet design where IVHM is considered an viii inherent system attribute, but it may also be added on to an existing design as part of a system retrofit. Clean-sheet approaches offer greater opportunities for design influence at the subsystem level, including integration of new IVH
32、M- specific sensors into vehicle buses and data recording systems. Maximum effectiveness of any IVHM solution can be gained only if the platform aspect of the solution integrates with operational decision support at the operators enterprise level, supporting similarly integrated maintenance, operati
33、ons, and logistics functions. Local subsystem data is typically reduced to the slices of collected “time series” data that are of interest to detecting or trending subsystem performance degradation (those slices are called features). This information may be combined with other information at the loc
34、al level, or provided to higher levels of system hierarchy. Here, inference or classification techniques may be used to assign human interpretable meaning that can trigger the remedial sets of events to mitigate the condition, or restore the system to healthy function. This implies that IVHM design
35、must consider a 3-dimensional application integration space, with integration occurring in the vertical (across levels of system indenture), horizontal (between cooperating components at a single level of indenture), and time (measures of system progression to failure and persistence of information)
36、, as illustrated in Figure 1. Certain nodes within this space must be loosely coupled so that system components can be plugged into and out of the architecture as system evolution occurs. Figure 1. IVHM Application Integration Space (Reprinted with permission from IEEE 2004 IEEE) 1 IVHM exploits the
37、 integration of the systems inherent design with enabling data acquisition and processing technologies. Distribution of the required IVHM functions requires an architectural framework that supports both the logical design and physical realization of the required functions and physical components. 1.
38、 Wilmering, T., “Approaches to Semantic Interoperability for Advanced Diagnostics Architectures.” Such an architecture came to be through an industrial partnership in the Open System Architecture for Condition Based Maintenance (OSA-CBM) Dual Use Science and Technology (DUST) program sponsored by th
39、e Office of Naval Research and the Program Executive Office Carriers. 2The OSA-CBM architecture is a framework that defines software components and interfaces used to compose a distributed health management system. A standard has been created: ISO 13374, 3that supports use of the integration methodo
40、logy by any supplier of platform subsystems or IVHM solutions, facilitating interoperability. OSA-CBM is a layered architecture that specifies seven layers defining various types of processing, with input/ output, data structures, functionality, and controls identified for each (see Figure 2). A wel
41、l-defined interface at each layer provides the flexibility to accommodate algorithms, open or proprietary, from any source. Figure 2. OSA-CBM Layered Reference Model 4 The interfaces at each OSA-CBM layer can be used to encapsulate each element in a participating distributed IVHM system, providing a
42、 disciplined plug-and-play approach to the required IVHM functions of: Sensing the required system performance data, acquiring it in a format that can be used for feature extraction and processing, then Transferring those features to higher order processing elements that 2. Kirby Keller, Andrew Bald
43、win, Stan Ofsthun, Kevin Swearingen, John Vian, Tim Wilmering, Zachary Williams, “Health Management Engineering Environment and Open Integration Platform”, Aerospace Conference 2007 IEEE, pp. 1-16, 2007, ISSN 1095- 323X. ONR Research Grant # N00014-00-1-0155. 3. ISO, International Standards Organiza
44、tion. 2003. “Condition monitoring and diagnostics of machines Data processing, communication and presentation Part 1: General guidelines.” ISO 13374-1:2003. ISO, International Standards Organization “Condition monitoring and diagnostics of machines Data processing, communication and presentation Par
45、t 2: Data processing”. ISO 13374-2: 2007. 4. Diagram by Dr. Ravi Rajamani, used with permission.ix Analyze through failure reasoning and prognostic analysis to classify feature progressions or thresholds to provide the information required to Act on the IVHM information via decision support and subs
46、equent enterprise processes. 4.0 Facets of IVHM Integration 4.1 Initial Approaches to Integrating Health Data Earliest attempts at health management integration took the form of a centralized-platform fault isolation and reporting system. In this approach, subsystem BIT results are sent to a central
47、ized diagnostic module, which typically relies on platform level models representing inter- and intra-system dependencies between subsystem failures. Processing the relationships between reported subsystem failures provides further fault isolation capability, and accounts for cross-system cascading
48、system failure effects. The resulting diagnoses are generally then classified as either flight critical or indicative of a maintenance issue requiring the operators attention. Thom and Flick, in the paper “Design Features of the 747-400 Electric Power System” 1989, describe a 747- 800 Electrical Pow
49、er System BIT system that was itself hierarchical with the EPS with the results of that processing sent to the onboard Central Maintenance Computer (CMC). The health monitoring strategy is for the Bus Control Units (BCUs) to monitor BIT from the Generator Control Units (GCUs) to resolve and classify system failures as belonging to a particular Line Replaceable Unit (LRU), with a 95% minimum confidence. The BCU BIT also determines whether the detected fault is flight critical or a maintenance issue. Critical failure information is sent to
