Abstract
Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.
Highlights
Bearings are regarded as the most common mechanical components in rotating machines
Our proposed method uses the Time Synchronous Averaging (TSA), Time Domain Features (TDFs), and Singular Value Decomposition (SVD) combination to extract the most relevant information from the raw vibration signal while the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to classify those features in order to identify several bearing defects under variable loads and speeds
In order to assess the impact of our proposed method before further investigation, Figure 8 displays a 3D projection of the first three singular values obtained by TSA, TDFs (SD and upper bound (UPP)), and SVD for the six bearing state under several loads and speeds
Summary
Bearings are regarded as the most common mechanical components in rotating machines. They are used to reduce the friction between all moving parts in the rotary devices such as alternators, compressors, turbines, etc. Guo and Wu used the signal envelope of the TSA to improve the signal-to-noise ratio in order to extract failure frequencies in planetary gearbox.[32] Ismail et al have combined TSA and Jerk energy to quantify the severity of bearing failures in electromechanical actuators (EMA).[33] many works have adopted time-energy indicators mainly: standard deviation (SD), entropy (EN), root mean square (RMS), upper bound (UPP), as timedomain features (TDFs) used in order to extract the most relevant information from the filtered signal and to describe the machine state These features, which are organized into matrices (feature matrices), have an extremely large dimension that may lead to serious troubles in the classification process and disrupt the most powerful classification algorithms, besides making the process considerably slow.
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