The instantaneous phase signal contains abundant information about the health state of machineries, which plays an important role in fault detection of machineries, rotor dynamic balance, transmission error of Rotate Vector reducer, etc. Traditional methods use signal processing methods based on encoders, EMD and Hilbert transforms or fractional delay filters to extract instantaneous phase information indirectly. These traditional methods are state-of-art, however those algorithms are slightly complicated and time-consuming, which is not conducive to online prognostics and health management to some extent. In fact, high precision and high reliability sensor technology can effectively reduce the complexity of subsequent signal processing. In this paper, an instantaneous phase direct detection technique based on eccentric demodulation principle is proposed. The method is derived from the kinematics between the cam and the pushrod. The laser displacement sensor is used to replace the pushrod to realize non-contact measurement. The instantaneous phase is modulation by the cam, and the proposed method can realize the obtain of the theoretical phase infinite resolution, which reaches 16,000-line resolution. Compared with the traditional encoder and other inspection technologies, the proposed method is also suitable for the measurement of broadband shifting and reciprocating motion. By means of simple signal processing methods, this paper realizes the fault diagnosis of the rolling bearing with typical defectives, and realizes the extraction and identification of the fault signal of the reciprocating compressor. Experimental results proved that the proposed inspection technology can serve as an effective technology for mechanical fault diagnosis.

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%.

Lubrication performance plays a key role in the lifetime of bearings. Online quantitative monitoring of the impurity contents of lubricants is an effective way to evaluate the performance of lubrication conditions. However, mainstream vibration monitoring techniques are often incapable of providing information on lubrication contamination especially for low-speed and high-load cases in which the dynamic interaction is insignificant. In this paper, an acoustic emission (AE) method is developed to achieve quantitative evaluation of the impurity content of lubrication greases, which are commonly used as lubricants for low-speed and heavy-duty bearings. In particular, a Peak-Hold-Down-Sample algorithm is proposed to compressively sample the large volume AE data acquired at the rate of several megahertz. Both simulations and experiments show that Peak-Hold-Down-Sampled AE data contain information about the deferent levels of impurities. Therefore, the proposed AE approach can be used to monitor lubrication performance in extreme operations.

High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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