Hierarchical symbolic analysis and particle swarm optimization based fault diagnosis model for rotating machineries with deep neural networks

Saravanakumar, R.; Krishnaraj, N.; Venkatraman, S.; Sivakumar, Bellie; Prasanna, S.; Shankar, K.

doi:10.1016/j.measurement.2020.108771

Cited by 21 publications

(18 citation statements)

References 15 publications

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“…The performance of the proposed model is simulated using the Python tool. To ensure the effective outcome of the presented model in the identification of various fault class labels, two datasets namely automotive gearbox and bearing fault from Case Western Reserve University Bearing Data Center [20]. The first dataset holds 7 types of health status like an outer race bearing fault, a minor chipped gear fault, a missed tooth gear fault, and three types of compound faults (Normal, Minor chipped tooth, Missing tooth (0.2 mm), and Missing tooth (2 mm)).…”

Section: Implementation Datamentioning

confidence: 99%

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

Surendran¹,

Khalaf²,

Romero³

2022

Computers, Materials &Amp; Continua

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In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

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Section: Implementation Datamentioning

confidence: 99%

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

Surendran¹,

Khalaf²,

Romero³

2022

Computers, Materials &Amp; Continua

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“…Hybrid model is a new prediction method which combines two or more neural network models, which has become the mainstream research trend. Among them, the hybrid model composed of CNN [ 8 , 9 , 10 ] and LSTM is the most common one in the field of RUL prediction of turbofan engine. CNN has a strong feature extraction ability, which cannot only extract local abstract features, but also process the data with multiple working conditions and multiple faults [ 11 , 12 , 13 ], especially the one-dimensional CNN can be well applied to the time series analysis generated by sensors (such as gyroscope or accelerometer data [ 14 , 15 , 16 ]).…”

Section: Introductionmentioning

confidence: 99%

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Cheng

Chen

et al. 2021

Sensors

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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“…Deep learning (DL) or deep neural network (DNN) is a branch of machine learning (ML) in which a neural network with many layers is exploited [10][11][12]. DL algorithms with deep structures have the ability to learn hierarchical representations directly from the input data.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

et al. 2021

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Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.

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Hierarchical symbolic analysis and particle swarm optimization based fault diagnosis model for rotating machineries with deep neural networks

Cited by 21 publications

References 15 publications

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

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