scholarly journals A Review on Deep Learning Applications in Prognostics and Health Management

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162415-162438 ◽  
Author(s):  
Liangwei Zhang ◽  
Jing Lin ◽  
Bin Liu ◽  
Zhicong Zhang ◽  
Xiaohui Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Management ◽  
Prognostics And Health Management

scholarly journals Physics Based Deep Learning Technique for Prognostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Khaled Akkad
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Health Management ◽  
Recognition Performance ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Learning Technique ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

scholarly journals A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

2019 ◽  
Vol 68 (2) ◽  
pp. 720-740 ◽  
Author(s):  
Andre Listou Ellefsen ◽  
Vilmar Asoy ◽  
Sergey Ushakov ◽  
Houxiang Zhang
Keyword(s):  
Deep Learning ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Comprehensive Survey

System-level prognostics and health management: A graph convolutional network–based framework

2020 ◽  
pp. 1748006X2093576 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett
Keyword(s):  
Deep Learning ◽  
Reliability Analysis ◽  
Health Management ◽  
Sensor Data ◽  
System Level ◽  
Health State ◽  
Prognostics And Health Management ◽  
Component Level ◽  
Convolutional Network ◽  
State Diagnosis

Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, resulting in a drawback when dealing with complex systems as the interactions among components are not explicitly taken into account. Here, we propose a system-level prognostics and health management framework based on geometrical deep learning where a system, its components with their interactions, and sensor data are represented as a graph. This enables reliability analysis at different hierarchical levels by means of (1) a system-level module for system health diagnosis and prognosis based on embeddings of the system’s learned features from a graph convolutional network; (2) a component-level module based on a deep graph convolutional network for health state diagnosis for the system’s components; (3) a component interactions module based on a graph convolutional network autoencoder that allows for the identification of interactions among components when the system is in a degraded state. The framework is exemplified via a case study involving a chlorine dioxide generation system, in which it is shown that integrating both components’ interactions and sensor data in the form of a graph improves health state diagnosis capabilities.

scholarly journals Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 83
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed ◽  
Kelvin Sundli
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Health Management ◽  
Main Task ◽  
Learning Approaches ◽  
Prognostics And Health Management ◽  
Normal Behaviour ◽  
Fault Detection And Identification ◽  
Industrial Level ◽  
Air Compressors

Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.

scholarly journals Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5888
Author(s):  
Joaquín Figueroa Barraza ◽  
Enrique López Droguett ◽  
Marcelo Ramos Martins
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Learning ◽  
High Performance ◽  
Deep Neural Networks ◽  
Health Management ◽  
Remaining Useful Life ◽  
Health State ◽  
Prognostics And Health Management ◽  
Selection Framework

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

scholarly journals Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhibin Zhao ◽  
Jingyao Wu ◽  
Tianfu Li ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Open Source ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Current Development ◽  
Diagnosis And Prognosis ◽  
Tremendous Progress ◽  
Challenges And Opportunities ◽  
Important Position

AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

A Review on Prognostics and Health Management : 2013~2018

2019 ◽  
Vol 19 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Hyun Su Sim ◽  
Jun-Gyu Kang ◽  
Yong Soo Kim
Keyword(s):  
Health Management ◽  
Prognostics And Health Management
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