A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering

This paper considers a quantitative method for assessment of fault severity of rolling element bearing by means of signal complexity and morphology filtering. The relationship between the complexity and bearing fault severity is explained. The improved morphology filtering is adopted to avoid the ambiguity between severity fault and the pure random noise since both of them will acquire higher complexity value. According to the attenuation signal characteristics of a faulty bearing the artificial immune optimization algorithm with the target of pulse index is used to obtain optimal filtering signal. Furthermore, complexity algorithm is revised to avoid the loss of weak impact signal. After largely removing noise and other unrelated signal components, the complexity value will be mostly affected by the bearing system and therefore may be adopted as a reliable quantitative bearing fault diagnosis method. Application of the proposed approach to the bearing fault signals has demonstrated that the improved morphology filtering and the complexity of signal can be used to adequately evaluate bearing fault severity

The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test

In-Process Quality Inspection of Rolling Element Bearings Based on the Measurement of Microelastic Deformation of Outer Ring

Quality inspection is the necessary procedure before bearings leaving manufacturing factories. A testing machine with low shaft speed and light radial load condition is generally used to test the dynamic quality of bearings, which avoids creating any potential damages to testing bearings. However, the signal of defective bearings is easily polluted by very weak noise using the traditional vibration-based measurement method due to the low shaft speed and light radial load condition specified for nondestructive inspection, which needs complicated and time-consuming calculation and is not suitable for online inspection. Thus, there are problems about special operating conditions and weak fault severity in quality inspection of bearings, which is quite different from the fault diagnosis of bearings. In this paper, a novel dynamic quality evaluation technique is proposed based on the measurement of Hertz deformations. The measurement system is mainly composed of an eddy current sensor, sensor fixture, and data acquisition platform with less transfer path than the vibration-based measurement system. The sensor fixture is optimized through numerical simulations to obtain signals with a high signal-to-noise ratio. Accurate evaluation of dynamic quality can be implemented reliably with simple signal processing. The proposed method can be used with a rotating speed of 100 rev/min and test load of 100 N, which is remarkably lower than the traditional quality inspection machineries with a rotating speed of around 1000 rev/min and the test load of 400 N. Both simulation and experiment studies have verified the proposed method

Application of Wavelet Packet Entropy Flow Manifold Learning in Bearing Factory Inspection Using the Ultrasonic Technique

For decades, bearing factory quality evaluation has been a key problem and the methods used are always static tests. This paper investigates the use of piezoelectric ultrasonic transducers (PUT) as dynamic diagnostic tools and a relevant signal classification technique, wavelet packet entropy (WPEntropy) flow manifold learning, for the evaluation of bearing factory quality. The data were analyzed using wavelet packet entropy (WPEntropy) flow manifold learning. The results showed that the ultrasonic technique with WPEntropy flow manifold learning was able to detect different types of defects on the bearing components. The test method and the proposed technique are described and the different signals are analyzed and discussed

Bearing fault diagnosis based on harmonic matching compensation and keyless phase order tracking

The vibration signals of the bearings of coal mine machanical equipment under the working conditions of strong impact and heavy load show strong transient non-stationary and local nonlinear features. It is difficult to identify the fault features by the classical time-domain statistical analysis method and the global domain transformation method. The traditional order tracking method has the problems of inconvenient equipment installation and difficulty in obtaining instantaneous frequency. The traditional keyless phase order tracking method estimates the instantaneous frequency with low precision under the condition of severe speed fluctuation. This leads to poor fault identification effect. To solve these problems, a new method of bearing fault diagnosis based on harmonic matching compensation and keyless phase order tracking is proposed. Firstly, the time-frequency analysis method based on harmonic matching compensation is used to process the bearing vibration signal and estimate the instantaneous frequency accurately. Secondly, the Vold-Kalman filtering method is used to adaptively extract the harmonic component signal. Thirdly, the Hilbert transform is used to calculate the instantaneous phase of the harmonic. The mapping relationship between the time domain and angle domain is obtained, so as to complete the resampling of the original time domain signal in the angle domain. Finally, the resampled signals are processed by fast Fourier transform (FFT). The fault features of the bearing are identified by analyzing the envelope order spectrum. The simulation and experimental results show that the maximum relative error between the estimated instantaneous frequency and the actual value is less than 1%. The feature order of bearing fault is accurate and obvious, which can effectively diagnose the bearing fault