An Automatic Feature Learning and Fault Diagnosis Method Based on Stacked Sparse Autoencoder

Qi, Yumei; Shen, Changqing; Liu, Jie; Li, Xuwei; Li, Dading; Zhu, Zhongkui

doi:10.1007/978-981-10-5768-7_39

Cited by 2 publications

(2 citation statements)

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“…In this type of auto-encoders, parameters’ set of encoder and decoder are trained on M data aiming input data reconstruction in output as well as satisfaction of sparse penalty function limitation through cost function L ( x , x ^ ) minimization. Training goal of SAE is finding θ and θ ′ in a way that minimizes following cost function (Figure 2) 22…”

Section: Methodsmentioning

confidence: 99%

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Induction motors fault diagnosis using a stacked sparse auto-encoder deep neural network

Jorkesh

Gholaminejad

Poshtan

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, deep neural network and stacked sparse auto-encoder deep neural network performances in fault diagnosis are compared. Methods are employed experimentally for the detection and isolation of an induction motor’s condition (healthy, bearing outer race fault, stator winding short circuit, and rotor broken bar) in the presence of unbalanced power supply and pump dry running disturbances. Pre-processing and de-noising is performed on three-phase electrical current signals using fast Fourier transform and independence component analysis algorithm, respectively. Experimental results show that sparse auto-encoder deep neural network method has outperformed and diagnosed the aforementioned faults in the presence of disturbances with a highly reliable accuracy rate of 90.65%.

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Section: Methodsmentioning

confidence: 99%

“…Mao et al 21 applied auto-encoder deep-learning machine using frequency spectrum for bearing fault diagnosis. Qi et al 22 made use of the stacked SAE neural network for rotary machine automatic fault detection. Sun et al 23 also used an SAE for feature extraction and then a neural network for induction motor's faults diagnosis.…”

Section: Introductionmentioning

confidence: 99%