Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

Abstract

Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

“…Thus, for the purpose of improving the search efficiency and easing the computational burden, the search center of cyclic frequency needs to be confirmed firstly, and the whole search process is carried out around this center. Spectral coherence provides a new interpretation of periodic flows of energy across the analysis frequency band and the cyclic frequency band [27]. With the help of its excellent ability in revealing the presence of modulation and describing the cyclostationarity, the fast spectral coherence is applied to accurately estimate the search center, and the relevance theory of fast spectral coherence can refer to literature [28].…”

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

“…Compared with SC, FSC not only has advantage of higher calculation efficiency, but also could capture cyclostationary signal much more accurately by refining frequency resolution arbitrarily. Kurtosis weighting was introduced to FSC to improve the performance of enhanced envelope spectrum (EES) [25]. However, FSC still has the defect of low robustness to strong background noise as being verified in the paper [23].…”

Time–Frequency Envelope Analysis for Fault Detection of Rotating Machinery Signals with Impulsive Noise

“…The cyclo-stationary (CS) process refers to a non-stationary process in which the statistical properties of the signal have periodicity. CS approach plays a very important role in extracting the faulty signature of a rotating machinery [7,8]. Envelope analysis has been widely used as a tool to analyze CS signals for a long time, especially as a powerful faultdetection technique for rolling bearings [9][10][11].…”

“…This method also helps diagnose faults using microphones, which are non-invasive sensors but are sensitive to background noise [5]. In the future, approaches of big data [8,37,38] and deep learning [39,40] will be needed to develop failure prediction and prognostic health management (PHM) technology.…”