Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings

Abstract

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method (E-CICA) is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform(DWT) method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio(ER) of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

“…Rolling bearings are an extremely vital part of mechanical equipment, whose running state determines directly the performance of the whole rotating mechanical system, so the research on bearing fault diagnosis is very important [1,2]. In recent years, the research methods about bearing fault diagnosis are mostly under the assumption of constant working condition [3][4][5].…”

Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing

“…This method selects the optimal dictionary from the limited wavelet candidates, which restricts the generalizability of this method. Compound fault diagnosis is a difficult problem in the field of fault diagnosis, and it is also an urgent problem to be addressed [ 15 ].…”

Target Signal Extraction Method Based on Enhanced ICA with Reference

“…However, in the process of optimization for the cICA algorithm, we need to set threshold parameters to distinguish target signals from other signals, which increases computation complexity and storage space and converges slowly. In some cases, the cICA algorithm cannot be converged [22][23][24][25].…”