Deep learning for prognostics and health management: State of the art, challenges, and opportunities

Rezaeianjouybari, Behnoush; Shang, Yi

doi:10.1016/j.measurement.2020.107929

Cited by 206 publications

(77 citation statements)

References 170 publications

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“…However, there are either the specified component (system) or the outdated techniques in the available research. As the DDML techniques in the NPP sharp a lot in recent years (Rezaeianjouybari and Shang, 2020;Yao et al, 2020;Saeed et al, 2020), there exists a gap in the current state-of-the-art of the DDML techniques for the FDD in the NPP. In this review, the current classifications, principles, characteristics, and applications of the FDD in the NPP, followed by the discussion on the future development of the DDML method for the NPP state prediction, will be illustrated.…”

Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

“…To achieve its goal, the nuclear industry has increased popularity in adapting the FDD techniques (Rezaeianjouybari and Shang, 2020). And the research process of the FDD methods in the NPP can be described as follows.…”

Section: Fault Detection and Diagnosis In Nppmentioning

confidence: 99%

“…The DNN technique has been proposed for the transient detection (Mo et al, 2007), PHM (Rezaeianjouybari and Shang, 2020), fault state detection of the solenoid operated valves (Utah and Jung, 2020), and the novel fault scheme (Saeed et al, 2020) in the NPP. In addition, Chae et al (2020) combined the longshort term memory (LSTM) network with the SVM and CNN approach to diagnose the pipe corrosion in the NPP.…”

Section: Deep Neural Network Techniquementioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

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Section: Data-driven Machine Learning Methodsmentioning

confidence: 99%

Section: Fault Detection and Diagnosis In Nppmentioning

confidence: 99%

Section: Deep Neural Network Techniquementioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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“…Although they are capable of capturing long-term temporal dependencies from the data, they have restrictions on long-term RUL predictions [ 32 ]. Small gradients tend to slowly shrink and eventually disappear during propagation across multiple unfoldings of network layers [ 33 ]. Popular variants of RNNs that avoid these limitations are Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks [ 31 , 34 , 35 , 36 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Bampoula

Siaterlis

Nikolakis

et al. 2021

Sensors

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Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

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“…PdM can be defined as a series of processes, where data is collected over time in order to monitor the state of equipment, in a manufacturing system. Ultimately, the goal is to identify/recognize patterns that in turn will facilitate engineers to predict and ultimately prevent failures (Rezaeianjouybari and Shang, 2020). Some of the most common problems that can be addressed with PdM include, the calculation of Remaining Useful Life (RUL), which aims at the suitable scheduling of future Maintenance and Repair Operations (MRO), Flagging Irregular Behavior, which is based on anomaly detection by the utilization of time series analysis, and Failure Diagnosis and Recommendation of Mitigation after failure (Lei et al, 2018;Mourtzis et al, 2020a).…”

Section: Machine Learningmentioning

confidence: 99%

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

2020

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The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.

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Deep learning for prognostics and health management: State of the art, challenges, and opportunities

Cited by 206 publications

References 170 publications

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

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