Abstract
To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults.
Highlights
As indispensable equipment in industrial production, rotating machinery plays an important role in the fields of transmission, transportation, precise control, and quantitative production and in ensuring the normal operations of rotating machinery
Given that engineering vibration signals contain a large amount of background noise, improving the signal-to-noise ratio (SNR) of the raw signal is a prerequisite for accurate diagnosis. e commonly used signal filtering methods can be divided into five categories
By synthesizing the above findings, this paper presents an automatic diagnosis method based on adaptive multiband filter and the stacked autoencoder (SAE) multiclassification model
Summary
As indispensable equipment in industrial production, rotating machinery plays an important role in the fields of transmission, transportation, precise control, and quantitative production and in ensuring the normal operations of rotating machinery. Erefore, detecting rotating machinery vibration signals and realizing an automatic diagnosis of structural faults are critical [1, 2]. E key to precisely diagnosing rotating machinery structural faults is to accurately extract the rotating frequency and its harmonics in the vibration signal. By synthesizing the above findings, this paper presents an automatic diagnosis method based on adaptive multiband filter and the SAE multiclassification model. (1) In the adaptive multiband filter, a search threshold is designed to accurately extract the actual rotating frequency, and the deflection coefficient is set to ensure that the harmonics are within the optimal extraction range (2) e SAE multiclass diagnosis model is built to adaptively extract the deep features of the vibrational signal and to realize an automatic diagnosis of rotating machinery structural faults e rest of this paper is organized as follows.
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