Jialong He

Bayesian method to solve the early failure period of numerical control machine tool

The hazard rate curve of the numerical control machine tool is a bathtub curve. The change point between the early failure period and the random failure period of the curve is difficult to obtain with a small data sample; thus, a Bayesian method is proposed. A method to build the prior distributions of the Weibull parameters is developed, which integrates the multi-source prior information of the target numerical control machine tool and the reference numerical control machine tool. The Markov chain Monte Carlo method is adopted to calculate the estimators of the Weibull parameters corresponding to each failure, which solves the problem of the absence of an analytical solution. The total working time of the numerical control machine tool when the estimator of the shape parameter is equal to 1 is estimated by taking the estimator of the shape parameter as the function of time. As a result, the change point and the early failure period are obtained. Comparison result shows that the result obtained through an existing change point solving method with a large dataset is close to the result generated through the proposed method with a small dataset. The change point and the early failure period obtained with the proposed method can be used to guide the early failure test and to design a rational maintenance strategy, which are of vital engineering significance.

Compilation of load spectrum for NC machine tool’s feed system based on the mixture weibull distribution

The load spectrum of the feed system for the numerical control (NC) machine tool is the objective basis of the reliability design and life prediction. So, a compilation method of load spectrum is proposed by using the mixture Weibull distribution (MWD). First, the load distribution in each feed direction is fitted by using the MWD. Among them, the Bayesian information criterion (BIC) determines the number of mixture components, the parameters of MWD are estimated by using the improved expectation maximization (IEM) algorithm, where the initial values of parameters are determined by the hidden Markov model (HMM). Second, the correlation between the loads in different feed directions is analyzed, and the joint distribution function (JDF) is established. Finally, the program loading spectrum is compiled by using the ratio coefficient. In this paper, the relative feed speed spectrum in the X-direction and Z-direction of NC lathes are established, case analysis results show that the relative feed speed distributions that use the proposed method have high fitting precision, and the load spectrum are compiled, which will be able to apply the reliability test, accelerated test and life prediction. Thus, the study of load spectrum has important theoretical significance and engineering application value.

Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

To obtain accurate computer numerical control lathe cutting force signals and improve the precision of load stress cycle statistic, an improved multi-wavelet denoising with neighboring coefficients method is proposed. First, statistical variance smoothing is applied to remove the singular points in the original signal. The processed signal is then denoised with the multi-wavelet with neighboring coefficients method. Second, based on the change laws of the correlation dimension and the values of Brock, Dechert, Scheinkman statistic of load signal, reasonable decomposition levels of the multi-wavelet and the length of neighboring coefficients are used. Third, four synthetic signals with different signal-to-noise ratios are denoised with the wavelet threshold denoising method and improved multi-wavelet denoising with neighboring coefficients. Then, the difference between the values of correlation dimension and Brock, Dechert, Scheinkman statistic in the original and denoised signals is analyzed. Meanwhile, its validity is further verified with the signal-to-noise ratio and mean square error. The results show that the improved multi-wavelet denoising with neighboring coefficients is better than wavelet threshold. Finally, turning force signals are denoised by wavelet threshold and improved multi-wavelet denoising with neighboring coefficients. Comparison result shows that the improved multi-wavelet denoising with neighboring coefficients can not only remain largely low-frequency signal energy and suppress high-frequency noise signals effectively but also improve the accuracy of load stress cycle statistic.

An efficient semi-analytical simulation for availability evaluation of discrete production lines with unreliable machines

This paper presents a semi-analytical simulation which combines analytical model and Monte Carlo technique to simulate the operation of discrete production lines. The purpose is to rapidly evaluate the availability for lines with unreliable machines and finite buffers in the design phase. Firstly, description of production line and availability definition are exhibited. Then, an output and inventory model is proposed to calculate the system output based on the failure data. With the help of Monte Carlo technique, the availability of the whole line in different system parameters can be obtained by iterating this analytical algorithm. Compared with real-time simulation, this semi-analytical simulation can remarkably reduce the computational load to failure number level without losing accuracy. Finally, numerical examples are carried out, and the results indicate that the proposed method is flexible and efficient by comparing with other four methods.