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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.

The vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectively decompose non-stationary and nonlinear signals, but it is not suitable for signal analysis of bearing with a complicated spectrum. In this paper, an improved EWT (IEWT) method is proposed by developing a new segmentation approach. Meanwhile, the IEWT is compared with empirical mode decomposition (EMD) and EWT to verify the superiority of IEWT in decomposition accuracy. By combining with the refined composite multiscale dispersion entropy (RCMDE), which is a powerful nonlinear tool for irregularity measurement of vibration signals, a new diagnosis method based on IEWT, RCMDE, multi-cluster feature selection and support vector machine is proposed. Then the method is applied to analysis of bearing in this paper and the results show that the new method has higher identifying rate and better performance than that of the methods of RCMDE combining with EMD or EWT. Also, the superiority of RCMDE to dispersion entropy and multiscale dispersion entropy is investigated, together with the superiority of MCFS for feature selection. INDEX TERMS Fault diagnosis, improved empirical wavelet transform, refined composite multiscale dispersion entropy, feature extraction, rolling bearing.

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.

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.

A novel non-contact instantaneous torque sensor is proposed in this paper. The mechanical structure of the torque sensor mainly consists of two eccentric sleeves rotating about an elastic shaft. The measurement of torque is transformed into the measurement of the phase difference between the eccentric sleeves. Eddy current sensors are used to measure distance changes between their probes and the eccentric sleeves. The phase is modulated by the distance changes when any torque applied to the elastic shaft the demodulation principle of the phase relies on solving simple trigonometric functions without any complex signal processing methods. Therefore, the acquisition of torque can be performed instantaneously without any accumulation of time or integer-period sampling. The proposed sensor has a simple structure with no electrical components within the rotational parts. Additionally, the proposed sensor facilitates the measurement of static torque, dynamic torque, and even reciprocating torque over a wide range of angular speeds. The sensor was calibrated by a torsion-testing setup and experimental results indicate that the sensitivity of the sensor is 23.05N m/ • , the sum of squares due to error is 0.09449, and the rootmean-squared error is 0.1375. The non-linearity is 0.914%. The proposed sensor accuracy is 0.06%.