Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation

Atamuradov, Vepa; Medjaher, Kamal; Dersin, Pierre; Lamoureux, Benjamin; Zerhouni, Noureddine

doi:10.36001/ijphm.2017.v8i3.2667

Cited by 107 publications

(115 citation statements)

References 102 publications

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“…To start with, ref. [73] reviews the state of practice and the state of the art of Prognostics and Health Management (PHM) strategies for OCL systems. Key sensors, monitoring parameters, state detection algorithms, diagnostics approaches and prognostics models are reviewed.…”

Section: Energy Sourcementioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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Section: Energy Sourcementioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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“…The process of data acquisition from the target equipment is necessary to make an accurate, reliable prediction of individual SSC health. Collected data can be either event or sensory data (Atamuradov et al, 2017). Event data are O&M logs containing actions taken by the operator or maintenance staff in response to events that occurred to the physical asset and are not the focus of this paper.…”

Section: Data Acquisition: Emerging Sensor Technologiesmentioning

confidence: 99%

“…It is a crucial step of the PHM framework, and the effectiveness of PHM largely depends on the accuracy of the CM process (Ayo-Imoru and Cilliers, 2018). The process of fault detection attempts to recognize incipient faults and failures 6 from CM data and quantification of the inconsistencies between the actual and the expected behavior of the monitored SSC in nominal conditions (Atamuradov et al, 2017). The instrumentation and control (I&C) systems in NPPs receive large amounts of sensory data from various components to enable and support safe and reliable power generation by controlling the system variables.…”

Section: Condition Monitoring and Fault Detectionmentioning

confidence: 99%

“…Prognostics is one of the major tasks in PHM as its results are directly used to support proactive decision-making for maintenance practices. The prognostics module is typically defined as the process of predicting the remaining time before the equipment can no longer perform a particular function (i.e., RUL) (Atamuradov et al, 2017). Prognostic calculations cannot be done in isolation and depend largely on the stages of monitoring, detection, and diagnostics: the accuracy of these stages will all affect RUL estimation.…”

Section: Prognosticsmentioning

confidence: 99%

“…As such, prognostic algorithms can be categorized according to different criteria. Based on the recent publications (Atamuradov et al, 2017;Lei et al, 2018;Taheri et al, 2019;Vogl et al, 2019;Baur et al, 2020;Bektas et al, 2020;Ramuhalli et al, 2020) that contain a comprehensive review of prognostics, these algorithms can be loosely divided into four categories according to their basic techniques or methodologies: physics-based methods, knowledge-based methods, data-driven methods, and hybrid methods.…”

Section: Prognosticsmentioning

confidence: 99%

See 2 more Smart Citations

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

Iacca

2022

Lecture Notes in Computer Science

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Deep neural networks (DNNs) obtained remarkable achievements in remaining useful life (RUL) prediction of industrial components. The architectures of these DNNs are usually determined empirically, usually with the goal of minimizing prediction error without considering the time needed for training. However, such a design process is timeconsuming as it is essentially based on trial-and-error. Moreover, this process may be inappropriate in those industrial applications where the DNN model should take into account not only the prediction accuracy but also the training computational cost. To address this challenge, we present a neural architecture search (NAS) technique based on an evolutionary algorithm (EA) that explores the combinatorial parameter space of a one-dimensional convolutional neural network (1-D CNN) to search for the best architectures in terms of a trade-off between RUL prediction error and number of trainable parameters. In particular, a novel way to accelerate the NAS is introduced in this paper. We successfully shorten the lengthy training process by making use of two techniques, namely architecture score without training and extrapolation of learning curves. We test our method on a recent benchmark dataset, the N-CMAPSS, on which we search for trade-off solutions (in terms of prediction error vs. number of trainable parameters) using NAS. The results show that our method considerably reduces the training time (and, as a consequence, the total time of the evolutionary search), yet successfully discovers architectures compromising the two objectives.

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Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation

Cited by 107 publications

References 102 publications

Data-Driven Fault Diagnosis for Electric Drives: A Review

Data-Driven Fault Diagnosis for Electric Drives: A Review

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

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