System Health Management (SHM) incorporates varies tools, technologies and techniques for automated detection, diagnostics and prognostics of system failures to improve efficiency (Jennions, et al., 2013). SHM may also be known as Integrated Vehicle Health Management (IVHM), Integrated System Health Management (ISHM), Prognostics and Health Management (PHM) and Health and Usage Monitoring (HUMS).
The overall aim of SHM is to generate information from data acquired by instrumentation which can be used to meet objectives such as improved system availability and reduced maintenance costs which are crucial for operational effectiveness and reducing downtime (Callan, et al., 2006; Dubicka, et al., 2011).
SHM systems help to reduce data traffic between sub-systems, improve diagnostics and provide estimation of remaining useful life through prognostic capability, reducing overall system support costs and resources (Black, et al., 2002).
SHM operation comprises three layers. Layer I – Sub-system health management monitors components at defined boundaries driven by fault detection and isolation, and inbuilt test functions (Scandura Jr, 2005). Layer II – A view of all sub-systems enables SHM to assess complete vehicle health for planning vehicle wide mitigation strategies (Scandura Jr, 2005). Layer III – Integration of SHM with operations provides optimum decision support to Vehicle Design, Mission Planning, Dispatch, Crew Alerting, Maintenance and Logistics (Scandura Jr, 2005).
An overview of SHM in operation for an industrial asset can be seen in Figure 1. This has been adapted from (Jennions, 2011) for subsea assets. During the design stage, faults and critical parameters are identified through FMECA and reliability availability and maintainability (RAM) analysis. Instrumentation and sensors are designed into the system to monitor these parameters during the operation and support phases of the asset lifecycle. Once the asset is in operation data can be acquired and measured. The data is transferred to storage and processing repository for analysis and used as inputs for diagnostic and prognostic models. The information gained from analysis and modelling can be used to determine the current state of health and predict remaining useful life. Operations support can use this information for maintaining reliability and integrity assurance and ensuring operational effectiveness. The diagnostic and prognostic information can be used by maintenance and supply chain for spares optimisation and allocation, maintenance, scheduling and tracking asset performance. Engineering design groups can use operational data to improve future designs and for Identifying and improving qualification tests.
Figure 1 – SHM in Operation
A typical hierarchy of aerospace SHM components can be seen in Figure 2. This has been adapted from (NASA, 2009) to show the typical elements SHM is comprised of.
Figure 2 – Typical SHM System Hierarchy
At the foundation level sensors and instrumentation measure and acquire data. The data is analysed and features of interest extracted. Data verification and validation will be performed to minimise false positive and false negative conditions and observe system states. Modelling is used to understand system behaviour or physics of failure. Information gained from modelling and analysis is used for detecting and isolating faults, and for prognostics to estimate the remaining useful life. Levels 1 to 3 form the core of the different health management (HM) sub-systems (Level 4) operating within an asset. The HM sub-systems generate key performance indicators which can be used to inform decisions on reliability, integrity and operational assurance.
Main Benefits of SHM
If the degradation or deviation of equipment from normal operating parameters can be assessed within its environment and the remaining useful life calculated, this information can be used to achieve the following objectives (Pecht, 2008):
- Provide Fault detection, Fault Isolation and Fault Prediction (including calculations of remaining useful life) on system’s failures.
- Support an informed transition toward condition-based maintenance by considering operational reliability data.
- Optimise equipment inspection, maintenance and spares allocation over the entire life cycle.
- Improve system availability by using operational reliability data and reduce lifecycle costs through condition-based maintenance, informed maintenance cycles, and extended operational usage with in situ monitoring capability.
- Provide information on operational and environmental load conditions for improving future designs, qualification testing and root cause analysis.
Black, S. et al., 2002. AN ARCHITECTURE TO IMPLEMENT INTEGRATED VEHICLE HEALTH MANAGEMENT SYSTEMS, s.l.: IEEE, Systems Readiness Technology Conference AUTOTESTCON Proceedings, ISBN 0-7803-7094-5, https://doi.org/10.1109/AUTEST.2001.948902.
Callan, R., Larder, B. & Sandiford, J., 2006. An integrated approach to the development of an intelligent integrated vehicle health management system, s.l.: Aerospace Conference IEEE, https://ieeexplore.ieee.org/document/1656070/, ISBN 0-7803-9545-8, DOI: 10.1109/AERO.2006.1656070.
Dubicka, A., Fan, I.-S. & Jennions, I., 2011. A BUSINESS ANALYSIS METHODOLOGY FOR INTEGRATED EQUIPMENT HEALTH MANAGEMENT IN HIGH VALUE ASSETS, s.l.: IEEE, Asset Management Conference, ISBN 978-1-84919-569-0, https://doi.org/10.1049/cp.2011.0572.
Jennions, I. K., Miguez-Esperon, M. & John, P., 2013. A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportunities, s.l.: Elsevier, Progress in Aerospace Sciences 56, pp 19-34, https://doi.org/10.1016/j.paerosci.2012.04.003.
Jennions, K. I., 2011. Integrated Vehicle Health Management – Perspectives on an Emerging Field. s.l.:Society of Automotive Engineers (SAE) International, ISBN 978-0-7680-6432-2, doi 10.4271/R405.
NASA, 2009. Integrated Vehicle Health Management (IVHM) Technical Plan. Version 2.03, ed. s.l.:National Aeronautics and Space Administration (NASA).
Pecht, M. G., 2008. Prognostics and Health Management of Electronics. ISBN 978-0-470-27802-4: John Wiley & Sons.
Scandura Jr, P. A., 2005. Integrated Vehicle Health Management As A System Engineering Discipline, s.l.: IEEE, Digital Avionics Systems Conference, https://doi.org/10.1109/DASC.2005.1563450.