This paper discusses the fault features selection using principal component analysis and using multi-class support vector machine (MSVM) for bearing faults classification. The bearings vibration signal is obtained from experiment in accordance with the following conditions: normal bearing, bearing with inner race fault, bearing with outer race fault and bearings with balls fault. Statistical pa...

Because of the importance of damage detection in manufacturing systems and other areas, many fault detection methods have been developed that are based on a vibration signal. Little work, however, has been reported in the literature on using a recurrence plot method to analyze the vibration signal for damage detection. In this paper, we develop a recurrence plot based fault detection method by ...

Quality control of textile products is an important stage in textile industries. To this end, the conventional method in fault detection is human inspection. In the present work, Wavelet transform was applied on images of simple circular knitted fabrics to diagnose five regular defects. The results showed that the method applied was accurate and fast in addition to being capable of determining ...

In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FBNSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFBNSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to el...

The design procedure of a second-generation wavelet packet decomposition, based on an evolutionary approach, is introduced for industrial fault detection. The procedure has been validated by means of an experimental case study for an induction motor used as traction machine in an elevator system. Preliminary results on three mechanical faults related to ball-bearing show encouraging performance.

Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the fe...

Extension neural network is a new type of neural network that combines extension theory and artificial neural network. Extension neural network has been applied to pattern recognition, fault diagnosis and clustering. According to fault characteristics of rolling bearing, we propose a fault diagnostic method for rolling bearing based on extension neural network. We construct the fault diagnosis ...

There are few studies on the fault diagnosis of deep learning in real large-scale bearings, such as wind turbine pitch bearings. We present a novel method, Bayesian augmented temporal convolutional network (BATCN), to filter raw signal bearing defect detection. This which employs neural networks, is designed capture dependencies signal, with focus non-stationary relationships collected signals....

In generally, detection and diagnosis of incipient faults is desirable for product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, the vibration and current of an induction motor are analyzed in order to obtain information for the detection of bearing faults. Significant vibration and current spectrum differences betwe...