For the spindle of machine tool, rotation error is a main factor affecting the machining quality, so many investigations have been focused on the measurement of the rotation error. In this paper, a measurement and separation method of the rotation error is proposed to extract the orbit of spindle section centre and to quantize the rotation error with a special measurement system. Thereinto, systematic measurement strategy has been especially designed to achieve the measurement and analysis of the rotation error. When the measurement does not conform to the small error conditions, the method can reduce the noise level from sensors and improve measurement accuracy in measuring the orbit of spindle section centre. When it is necessary to accurately quantify the rotation error and the roundness error, the method also can effectively separate them with independent coordinate system and sensor bracket. Additionally, considering the effect of high temperature on spindle, the method can reduce the form error caused by thermal expansion in the measurement process, which is very important in industrial online measurement. Finally, the theoretical analyses are shown good agreement with experimental results, and these also reveal that it is an effective measurement method for the rotation error.

In this paper, a novel method to diagnose eccentricity fault in rotor of three-phase induction motor based on the current signal, is proposed along with experimental validation. It utilizes three-phase current in time series into Clarks two phase α–β 90° apart vectors assimilating into 0° measuring reference frame starting point with the aid of key-phasor averaging method. α–β sinusoidal waves are applied to an interpolation method which gives an improved Fast Fourier transform of amplitude and phase angle of α–β components. These transformed two-phase current signals are again measured to get amplitude and phase. Finally, the amplitude and phase of α–β are used to generate holo-spectrum with an ellipse of the first order. Since unbalance fault occur at peak fundamental 1X harmonic frequency component, so its amplitude and phase angle are utilized for orbit construction. A comparison is made on rotor orbit and its eccentricity values, constructed from vibration signals and three-phase current signals, both measured simultaneously. This leads to a direct relationship in terms of orbit eccentricity and its longitudinal major axis inclination. The results show that at low frequency the eccentricity values of vibration and current signals orbits coincide hence the unbalance fault is detectable in induction motor by only utilizing the current signals. This method represents rotor non-linear dynamic behavior and is immune to unsymmetrical and anisotropic surface properties of the rotor and can measure rotor condition representing an accurate severity level of air gap eccentricity or, in mechanical terms, rotor unbalance fault. The new technique is a progressive development in induction motor air gap eccentricity fault detection and is applicable to harsh running conditions due to its simplified and non-invasive approach.

Wheel is an important component of bogie, and its geometry affects the dynamic behaviors of rail vehicles. To accurately obtain the geometric feature information of wheel tread profile, an on-machine measurement method with the 1-D laser sensor is investigated in this article. Combined with the specific morphology of tread profile and measurement model of laser sensor, the corresponding on-machine measurement model of wheel tread profile is established according to the characteristics of inclined and curved profiles. To improve the measurement accuracy, an adaptive error correction is studied to strengthen the measurement of complex profiles and suppress the measurement error of laser sensors. And the method achieves error suppression by more than 30% in the test and calibration experiments. Finally, the measurement method based on the laser sensor is put into the practical measurement of the wheel tread profile on an underfloor wheelset lathe, and the effectiveness of the proposed method is verified with experiments. These works can not only improve the measurement accuracy of wheel geometry but also enhance the machining capability of machine tool, which is important for the safe operation of rail vehicles.

The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are suitable for matrix processing but challenged by the higher-order data. To tackle these problems, a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization is presented in this paper. Primarily, the phase space reconstruction and the short time Fourier transform are successively employed to convert the original signal into time-frequency distributions, which are further arranged into a three-way tensor to obtain a time-frequency multi-aspect array. The tensor is decomposed by sparse non-negative tensor factorization via hierarchical alternating least squares algorithm, after which the latent components are reconstructed from the factors by the inverse short time Fourier transform and eventually help extract the impulse feature through envelope analysis. For performance verification, the experimental analysis on the bearing datasets and the swashplate piston pump has confirmed the effectiveness of the proposed method. Comparisons to the traditional methods, including maximum correlated kurtosis deconvolution, singular value decomposition, and maximum spectrum kurtosis, also suggest its better performance of feature extraction.

Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.