Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

Qu, Yongzhi; Zhang, Yue; He, M.; He, David; Jiao, Chen; Zhou, Zude

doi:10.1177/1748006x18822447

Cited by 14 publications

(13 citation statements)

References 37 publications

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“…Autoencoders for anomaly detection are frequently used in problems of health state diagnostics 70,73,79 and prognostics 84,97 where the dataset is unbalanced, which makes the training process more challenging.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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Deep learning health state prognostics of physical assets in the Oil and Gas industry

Barraza

Brauning

Pérez

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies play a crucial role in the avoidance of unplanned downtimes and enhancement of productivity. In particular, Condition-Based Maintenance is an approach in which maintenance actions are performed depending on the assets’ health state that is evaluated through different kinds of sensors. In this paper, Deep Learning methods are explored and different models are proposed for health state prognostics of physical assets in two real-life cases from the Oil and Gas industry: a Natural Gas treatment plant in an offshore production platform where elevated levels of CO2 must be predicted, and a sea water injection pump for oil extraction stimulation, in which several degradation levels must be predicted. A general methodology for preprocessing the available multi-sensor data and developing proper models is proposed and apply in both case studies. In the first one, a LSTM autoencoder is developed, achieving precision values over 83.5% when predicting anomalous states up to 8 h ahead. In the second case study, a CNN-LSTM model is proposed for the pump’s health state prognostics 48 h ahead, achieving precision values above 99% for all possible pump health states.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…As with Machine Learning algorithms, DL has been also applied to CBM to perform both diagnosis 62–75 and prognosis. 76–87 Chen et al 71 use CNNs to identify faults in a gearbox.…”

Section: Introductionmentioning

confidence: 99%

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Barraza

Brauning

Pérez

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Three levels of damage were estimated. The approach in [65] uses Stacked Auto Encoders (SAE) for unsupervised feature extraction, feature reduction based on QR decomposition is then applied on the feature matrix provided by the SAE to obtain low dimensional data feeding an unsupervised K-means clustering.…”

Section: Previous Workmentioning

confidence: 99%

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Cerrada

Trujillo

Hernández

et al. 2022

MCA

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Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.

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“…Various studies have recommended and demonstrated successfully the use of pre-trained network models. 31–37 In the present study, the feature extraction properties of several renowned CNN architectures like VGG 16, 18 GoogLeNet, 38 AlexNet, 17 and ResNet50 20 were evaluated with the help of machine learning classifiers. A brief description of the CNN models considered are described in this section.…”

Section: Machine Learning Based Three Phase Approach For Fault Detection In Aerial Imagesmentioning

confidence: 99%

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Venkatesh

Sugumaran

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.

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Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

Cited by 14 publications

References 37 publications

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Deep learning health state prognostics of physical assets in the Oil and Gas industry

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

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