A hybrid deep-learning model for fault diagnosis of rolling bearings

Yang, Xu; Li, Zhixiong; Wang, Shuqing; Li, Weihua; Sarkodie-Gyan, Thompson; Feng, Shizhe

doi:10.1016/j.measurement.2020.108502

Cited by 172 publications

(65 citation statements)

References 39 publications

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“…For example, [35] combines a NN with clustering to predict the daily electric peak load. Other researchers combine supervised and unsupervised data-driven methods to implement an automated FDD process in an air handling unit [36], or to combine a convolutional neural network (CNN) with deep forest (gcForest) for fault diagnostics of rolling bearings [37]. Many researchers combine model-based and data-driven models [38][39][40][41][42].…”

Section: Hybrid Model-based Approachesmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

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Section: Hybrid Model-based Approachesmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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“…Rolling bearings can fail at any time due to fatigue, even if they are operated under ideal design conditions. If the damage to rolling bearings can be predicted in advance, it is possible to prevent failure and stop the rotating machine, thereby securing the operation reliability of the machine, and this can have a positive effect on the quality of the product [13,14]. It is beneficial to find the cause of bearing damage early, but it is difficult to ascertain the true cause of bearing damage.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments

Han

Jeong

2021

Applied Sciences

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The failure of a facility to produce a product can have significant impacts on the quality of the product. Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. In this paper, we propose a denoising autoencoder (DAE) and multi-scale convolution recurrent neural network (MS-CRNN), wherein the DAE accurately inspects bearing defects in the same environment as bearing vibration signals in the field, and the MS-CRNN inspects and classifies defects. We experimented with adding random noise to create a dataset that resembled noisy manufacturing installations in the field. From the results of the experiment, the accuracy of the proposed method was more than 90%, proving that it is an algorithm that can be applied in the field.

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“…The combination of ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), and singular value decomposition (SVD) technique is introduced in [ 7 ]. The combination of convolutional neural network (CNN) model and a deep forest (gcForest) model is used to fault diagnosis of bearing and proposed in [ 8 ]. On the other hand, data-driven techniques have the challenges of unreliability, especially for accurately characterizing nonlinear and non-stationary signals, as well as high dependences on the type and accuracy of the data [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Crack Size Identification for Bearings Using an Adaptive Digital Twin

Piltan

Kim

2021

Sensors

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In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.

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A hybrid deep-learning model for fault diagnosis of rolling bearings

Cited by 172 publications

References 39 publications

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments

Crack Size Identification for Bearings Using an Adaptive Digital Twin

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