New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

Abstract

Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases.

Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system

“…Moreover, the variation in load and speed of the rotating machine will eventually lead to inaccurate classification results. 23 Currently, intelligent classification techniques are the outstanding methods for bearings condition monitoring. [23][24][25][26] Intelligent classification strategies contain two main steps: feature extraction and intelligent classification.…”

“…23 Currently, intelligent classification techniques are the outstanding methods for bearings condition monitoring. [23][24][25][26] Intelligent classification strategies contain two main steps: feature extraction and intelligent classification. The features extraction is the key step part in fault recognition and identification; an accurate features extraction process allows the extraction of the most relevant information from the raw vibration signal.…”

Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

“…Short-term Fourier Transform (STFT), [2][3][4] Wigner Ville Distribution (WVD), 5,6 Wavelet Transform (WT), 7,8 Hilbert-Huang Transform (HHT), [9][10][11] and Hilbert Empirical Wavelet Transform (HEWT) 12,13 were the most commonly used timefrequency techniques, and recently Fast Kurtogram (FK) [14][15][16] and Autogram. [17][18][19][20] However, signal processing methods have the ability to detect the occurrence of a bearing failure, but without providing any information about the defect nature. Moreover, the variation in the rotating machine's load and speed will eventually lead to inaccurate results.…”

“…20 Currently, intelligent classification techniques have gained popularity in numerous industrial applications. [19][20][21][22][23][24][25][26][27] Fuzzy logic System (FLS) and artificial neural network (ANN) have been effectively used for gear and bearing diagnosis. Fuzzy and neural techniques are soft computing approaches which can be easily implemented for complex systems to monitor their behaviors.…”

“…By simulating human thinking, ANN has carried various advantages in detecting and identifying different defect types without having an expert knowledge of the system. 20 However, ANN requires high-performance processors with parallel processing power according to their structure. 28 However, no specific rules are available to define the ANN structure but an appropriate network structure can be achieved by experience.…”