Abstract
Parallel with significant growth in industry, especially mysteries related to energy engineering, condition monitoring of rotating systems have been experiencing a noticeable increase. One of the prevalent faults in these systems is fatigue crack, so finding reliable procedures in identification of cracks in rotating shafts has become a pressing problem among engineers during recent decades. While a vast majority of cracked rotors can operate for a specific period of time, to prevent catastrophic failures, crack detection and measuring its characteristics (i.e. size and its location) seem to be essential. In the present essay, a hybrid procedure, consisting of Deep Learning and Discrete Wavelet transform (DWT), is applied in detection of a breathing transverse crack and its depth in a rotor-bearing-disk system. DWT with Daubechies 32(db32) as wavelet mother function is applied in signal noise reduction until level 6, also its Relative Wavelet Energy (RWE) and Wavelet entropy (WE) are extracted. A characteristic vector that is a combination of RWE and WE is considered as input to a multi-layer Artificial Neural Network (ANN). In this supervised learning classifier, a multi-layer Perceptron neural network is used; in addition, Rectified Linear Unit (ReLU) function is exerted as activation function in both hidden and output layers. By comparing the results, it can be seen that the applied procedure has strong capacity in identification of crack and its size in the rotor system.
Highlights
Because of a rapid growth in industry and technology, a vast majority of machines work in high-velocity, so potential faults in these devices can bring about many detriments
A hybrid procedure consisting of discrete wavelet transform and deep learning procedures are employed in classifying cracked shafts in a rotating system with various crack depths
At the initial step of signal processing, collected signals are noise reduced by the help of discrete wavelet method to level 6
Summary
Because of a rapid growth in industry and technology, a vast majority of machines work in high-velocity, so potential faults in these devices can bring about many detriments. Rotating systems are one of the most widely used devices in modern and classic industries, for this reason analyzing these machines has become a favorite task among engineers. Numerous faults can occur in this system, but bearing and shaft’s faults are more rampant. Faults such as misalignment, cracks and rotor to stator rub can occur concertedly in rotor bearing systems. Since the 70’s, many scholars have been working on crack identification methods in rotating systems. During the two last decades crack identification procedures witnessed noticeable developments, and, they have concentrated focus on vibration analyzing [2]
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