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Sensor Systems for Prognostics and Health Management
Open accessAbstract
Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
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![Figure 1. An example of PHM application for an automobile [15].](/paper-figures/a01c5a00bfe54fa09441bd2fe5c5a39d/sensor-systems-for-prognostics-and-health-management-figure-1.jpg)
![Figure 2. Integrated sensor system for PHM [17].](/paper-figures/87550a242a074410b46788cce2465498/sensor-systems-for-prognostics-and-health-management-figure-2.jpg)
![Figure 3. Flowchart of FMMEA process [18,19].](/paper-figures/420bc8106277433aac3aac67f8e13a58/sensor-systems-for-prognostics-and-health-management-figure-3.jpg)
![Figure 4. Schematic figure of PME-based MLCC [21].](/paper-figures/64a2107e189c4f9b9f4717cd9ed818fa/sensor-systems-for-prognostics-and-health-management-figure-4.jpg)
![Figure 5. ePrognostic sensor system [17].](/paper-figures/0aaeffedb42d402cb2fd98984f9f8458/sensor-systems-for-prognostics-and-health-management-figure-5.jpg)
![Figure 6. Example of sensing location [36] (Sensor card is put in the express card slot of a laptop).](/paper-figures/05a64a4f3c8341eb943bd7d0e471b982/sensor-systems-for-prognostics-and-health-management-figure-6.jpg)
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“To sum up, these are the important issues when developing a PHM strategy for AV technology [49].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [49]
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Metadata
- Authors
- Cheng, S., Azarian, M. H., & Pecht, M. G.
- Affiliations
- University of Maryland, College Park (US)
- City University of Hong Kong (HK)
- Corresponding author
- Pecht, Michael G.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2010-06-08
- Volume
- 10
- Issue
- 6
- Pages
- 5774-5797
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3247731
- OpenAlex
- W2149823865
- Cited by
- 188
- References
- 56
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 9 · 2023: 8 · 2022: 10 · 2021: 13 · 2020: 13 · 2019: 11 · 2018: 17 · 2017: 9 · 2016: 16 · 2015: 16 · 2014: 11 · 2013: 12 · 2012: 18
- FWCI
- 6.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CityU Scholars (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Physics and Astronomy › Instrumentation › Sensor Technology and Measurement Systems
- Keywords
- prognostics and health management, sensor systems, sensor selection, remaining useful life prediction, product reliability, fault detection, sensor reliability, sensor cost, condition monitoring
- MeSH terms
- Computer Communication Networks, Health, Humans, Models, Biological, Monitoring, Physiologic, Prognosis, Biosensing Techniques, Delivery of Health Care, Integrated, Disease Management
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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