Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis

Zheng, Longkui; Xiang, Yang; Cui, Sheng

doi:10.1007/s40430-019-2011-5

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…Rotating machinery plays a signifcant role in modern society, generally being used in aerospace, transport, and industrial production, and therefore must ensure that the machinery has a high degree of safety and reliability. Rolling bearings are one of the critical components of rotating machinery, and their operating condition will directly afect the working condition of rotating machinery [1]. Researchers have proposed predictive and healthy management (PHM) to maintain rotating machinery and promote its reliability and safety to guarantee the healthy operation of rotating machinery over a long period.…”

Section: Introductionmentioning

confidence: 99%

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Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

Jiang

Xiang

2023

Shock and Vibration

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As a significant component of rotation machinery, bearing plays a role in supporting and transmitting power. However, bearings are subject to complex operating conditions and are prone to failure. To avoid ineffectiveness and improve the reliability of bearings, a data-driven method is used to predict the remaining useful life (RUL). However, this method is less stable and can only forecast the RUL of bearings under training sample conditions. An ensemble deep, long-term, and short-term memory (EDLSTM) method is proposed to solve this problem. First, the feature of the forecast-bearing RUL was extracted including time-domain features, frequency-domain energy features, and Shannon entropy. Then, a deep long- and short-term memory network prediction model of the bearing RUL was constructed. To resolve the instability of DLSTM predictions, multiple DLSTMs were ensembled using the maximum information component (MIC) criterion. The model i trained using bearing data with different failure modes under difficult operating conditions to improve the predictive stability of the model. Finally, an EDLSTM was constructed to achieve the bearing RUL prediction. In the prediction result of the training set, the cumulative relative accuracy (CRA) was above 0.9 for most of the bearings. According to the experimental results in the test set, the mean CRA was over 0.80. For some of the bearing’s RUL, the CRA was more than 0.90. The above results show that the proposed approach can effectively predict the RUL of a bearing and has a more stable prediction ability than the bagging integration method.

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Section: Introductionmentioning

confidence: 99%

“…To improve the reliability of bearings, Zheng et al [1] used K-ELM to identify faults in the bearings. Zhang et al [4] proposed a ResNet-STAC-Tanh fault diagnosis method for the bearing.…”

Section: Introductionmentioning

confidence: 99%

Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

Jiang

Xiang

2023

Shock and Vibration

Self Cite

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“…With the rapid development of the economy and technology, a large number of optimization problems have appeared in scientific research and actual industrial production, such as the transportation field [1], [2], the power field [3], [4], the material field [5], [6], the communication field [7], [8], the mechanical field [9], [10] and e-commerce field [11], [12], and etc. These problems are usually inferior in nature, that is, non-differentiable, non-linear, etc.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mayfly Optimization Algorithm Based on Median Position and Its Application in the Optimization of PID Parameters of Hydro-Turbine Governor

Guo

Yu³

et al. 2022

IEEE Access

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Mayfly algorithm is a new intelligent optimization algorithm with unique optimization capabilities recently proposed. It has strong research value, but there are also insufficient explorations, and it is easy to fall into the problem of local optimization. This paper aims to improve the optimization performance of the mayfly algorithm and explore its application value in practical engineering optimization problems. An improved mayfly algorithm based on the median position of the group is proposed. In its velocity update, the median position of the group is introduced with emphasis, and a non-linear gravity coefficient is introduced at the same time. Through the benchmark test function, its superiority in exploitation, convergence speed and accuracy and the improvement of exploration are verified. At the same time, the simulation model of the hydro-turbine governor using MATLAB/Simulink is established, and 10% frequency disturbance experiments of this model are carried out separately in two typical working conditions. The experiments results show that the optimal ITAE index value of the system obtained by the improved mayfly algorithm is smaller, and 16.5 and 18.1 iterations to complete on average. In addition, the experiments results reveal that the PID parameters optimized by the improved mayfly algorithm can make the dynamic performance of the regulation system better than other popular swarm intelligence algorithms, where the overshoot decreased by more than 3.1%, and the adjustment time also decreased in different degrees. The proposal of the median position of the group provides a new idea for the improvement of the swarm intelligence optimization algorithm. Meanwhile, a new effective method for optimizing the PID parameters of the hydro-turbine governor has been found.

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“…In [5], authors introduce a study of fault diagnosis of a low-speed bearing based on acoustic emission signal and multi-class relevance vector machine. In [6], an optimization method was applied to diagnose rolling bearing malfunctions, such as an optimization-based improved kernel novel method based on machine learning. At the same time, reducing the dimension of output values makes sense when assessing signs of machine part malfunctions, although it is difficult to determine the main influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Fault Detecting Accuracy of Mechanical Damages in Rolling Bearings

Kudelina

Baraškova²,

Shirokova³

et al. 2022

Machines

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Electrical machines are to face different challenging factors during operation, such as high unexpected or excessive loads, unusual properties of the working environment, or intense fluctuations in rotation speed. Therefore, maintenance questions and predicting the accuracy of an equipment’s condition have great importance. This study is based on the theory of vibration reliability. This article introduces the most common faults of bearings in electrical machines and discusses their diagnostic possibilities. Experimental setup, as well as studied bearing failures, are described. The accuracy of conducted experiments is introduced.

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Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis

Cited by 13 publications

References 32 publications

Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

An Improved Mayfly Optimization Algorithm Based on Median Position and Its Application in the Optimization of PID Parameters of Hydro-Turbine Governor

Fault Detecting Accuracy of Mechanical Damages in Rolling Bearings

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