Methods of reliability estimation for numerical control machine tool based on performance parameters

In view of the characteristics of energy transmission in the running processes of numerical control (NC) machine tool driven by variable voltage variable frequency (VVVF), an energy flow model of main driving system is established. The power transition characteristics of main motor and mechanical transmission system and the energy loss on every section are analyzed. Finally, the dynamic power balance equation of the whole main drive system is established. Comparing with the equation of common machine tools, this equation has three characteristics. The wide application prospect of the equation is shown by the application analysis and practices.

Traditional life evaluation theory established on the basis of mass failure data, the phenomena of little or naught failure put forward challenges for existing life evaluation theory. The performance degradation data provides useful information for products' life and reliability, and gives feasible way for life evaluation. The limitations of existing stochastic process models for performance degradation are analyzed; a new approach based on performance degradation amplitude is brought forward, the calculation methods based on degradation amplitude analysis are given out. For degradation product with large amplitude size, by way of fitting the reliability of every moments, the performance degradation rule can be got and the products' life can be forecast; for small amplitude size, the degradation path is selected in accordance to engineering experience and products' failure mechanism, the products' expected life and reliability is evaluated based on appropriate distribution model. An example of laser device with performance degradation data is given out to validate the effectiveness of the method.

As few or no failures occur during life test and accelerated life test for long-lifetime products, FR-4 printed circuit boards (PCBs) are employed as research objects to conduct performance degradation test under high temperature and humidity. Based on the analysis of the failure mode, failure mechanism and the study on the degradation parameters of PCB, the degradation failure model of PCB is built which is related to bias voltage in high temperature and humidity. Based on the statistical analysis method on performance degradation data, a reliability evaluation method of degradation paths is developed for reliability inference of PCB. Finally, the statistical result about pseudo-failure lifetime data under Weibull distribution is given, which confirms its validity and effectiveness.

In minimal repair,in order to describe the failure process of numerical control(NC) machine tools experiencing both early failures and deterioration phenomena,and operating so long that the intensity function approaches a finite asymptote as the system operating time grows,a new four-parameter non-homogeneous Poisson process model is proposed.The characteristics of the model are discussed,and the physical meaning of its parameters is given,calculating formula of point estimation for model parameters and reliability indices such as the minimum intensity value and the time of minimum intensity are all derived,then a method of model selection for reliability assessment of two NC machine tools is provided based on the likelihood ratio statistic,and the model evaluation criterion is given based on the index of goodness-of-fit test.The real failure data of two NC machine tools are analyzed by using the proposed method,the results show that this method can quantitatively evaluate the machine tools' lasting time of early failure period,it is suitable for describing the failure process of NC machine tools with bounded and bathtub shaped failure trend,and can provide a theoretical basis for eliminating as much early failures as possible before the NC machine tools leave the factory.

As the rapid development of modern technology, industrial companies have to manufacture high-reliability and long-lifetime products to satisfy the changing needs of the customers. In such cases, it is difficult to assess the reliability and lifetime of the product with traditional life testing. Thus how to evaluate the lifetime and reliability index of these products is an urgent problem to be solved. For some products, it is possible to obtain the performance degradation information by the accelerated degradation testing (ADT). Therefore, to utilize the product degradation information may be an effective way to resolve this issue. However, most of lifetime prediction methods in use are mainly based on single prediction model with the disadvantage of low robustness and inaccuracy, so it cannot get the high credible lifetime of the product. This paper will adopt the prediction thought of combining two single models to establish a new prediction model, also propose a combined life prediction method based on the performance degradation data by using the particle swarm optimization (PSO) with immunity algorithms (IA). The author selects two better prediction models, which are time series model and the model of Brownian motion with drift, to predict the degradation path of the product separately. As the degradation process of the product is stochastic, and these two models are also well suited for this process. So each of them is good for long-term prediction. Then the objective function of the sum of the absolute errors is established with weight coefficients which are used to combine these prediction methods and integrate the prediction results. And the objective function is minimized with the IA-PSO, which has the characteristics of fast global optimization and uses to determine the weight coefficients for each of the prediction models. This approach can reduce the blindness of calculation and improve the accuracy of the prediction model. To verify this combined prediction method, the application of SLD's performance degradation data is demonstrated in this paper, the result shows that the combined prediction method based on the IA-PSO is effective and reasonable, and evidently has less prediction errors than each of single model. Therefore, this method plays an instructive way in engineering practice and research field.

Reliability assessment method based on performance degradation data has been proven to be useful when dealing with products characterized as long-life and highly reliable. However, this method can hardly be applied when measurement of performance degradation is technically difficult or costly. To tackle this problem, this paper proposes to utilize the measurement data which are related to the performance degradation, namely, marker data. It is assumed that the degradation process is modeled with Wiener process, and that the marker is the integral of the performance degradation. We first estimate the parameters of the Wiener process using marker data and then conduct the reliability assessment. A simulated example is presented to demonstrate the proposed method.

The reliability of machine tool is getting more and more attention. Reasonable reliability allocation is beneficial to improve the inherent reliability of machine tool. However, the existing reliability allocation methods for machine tool have some limitations. For example, static part is selected as the reliability allocation object of machine tool, which cannot reflect the characteristics “function realized by motion”; factors affecting reliability allocation are not considered comprehensively, weights of experts are treated as the same, and the allocation results are not optimized or the impact of time on enterprise is neglected in the optimization. To solve these problems, a new multiobjective optimization reliability allocation method for machine tool is proposed in this paper. Firstly, the latest achievements about meta‐action are given, and meta‐action is set as the reliability allocation object. Secondly, more reasonable and comprehensive factors affecting reliability allocation are extracted. Thirdly, expert weight coefficient is brought to reduce the subjective impact of expert scoring. Fourthly, time factor is brought to make the optimized allocation results more reasonable and accurate. Finally, a numerical control (NC) machine tool made in China is taken as an example, with a comparison on the reliability allocation results of current methods and proposed method. The results verify the applicability, rationality, and accuracy of the proposed method, which lays a foundation for the subsequent study on the quality characteristics of machine tool based on meta‐action.

Reliability test for a certain numerical control machine tool is carried out. Failure data is collected. On this basis, a reliability model is established. Inference is a Weibull distribution. The reliability model is also analyzed. The weighted least squares method is used to estimate the scale parameter alpha and the shape parameter beta of Weibull distribution. Research shows, this method can effectively establish the reliability model.

With the continuous development and application of digital technology, CNC machine tools have become the main production equipment in the manufacturing industry. CNC machine tools are developed on the basis of the theory of CNC technology, but it is necessary to design the system with some computer-aided design to achieve automatic management. This paper analyzes the computer aided technology and numerical control technology applied in the numerical control machine tool error analysis of the practical application of, and by using multiple regression analysis method for the computation of the efficiency and quality of application of computer aided technology and numerical control technology of CNC machine tool error analysis on the efficiency and quality of data with 15% and 45% respectively than traditional CNC machine analysis, proves that computer aided technology and numerical control technology for numerical control machine tool error analysis plays an important role.

The point maximum likelihood and interval estimators of the parameters, as well as two reliability indices of the log-linear process model, including cumulative mean time between failures and reliability at given time are given. In tests for failure time trends, both the graphical methods include the mean cumulative function versus time plot and the total-time-on-test plot, and the analytical methods include the Laplace, the military handbook, and the Lewis-Robinson tests are used. A real case of failure dada with time truncation for multiple numerical control (NC) machine tools is given to illustrate the use of the proposed model.

The existing methods of determining testing period do not consider the effect of working condition covariates on reliability testing period of Numerical Control (NC) machine tools, which may lead to high-cost testing or low-precision assessment. Aiming at the problem, a new method of determining the testing period considering working condition covariates is proposed. The change rate of interval estimation of Mean Time between Failures (MTBF) is used as the criterion for determining the length of testing period. The reliability model of NC machine tools is established by the Cox proportional hazards model, and the two-step estimation method is used to estimate parameters of the baseline failure rate function and the coefficients of working condition covariates. The Bootstrap resamples are obtained by the Bootstrap resampling method. And then the parameters of the baseline failure rate function and the coefficients of working condition covariates are estimated simultaneously by maximum likelihood method, and thus interval estimations of MTBF under each covariate are obtained. The change rate models of MTBF interval estimation under each covariate level are established by Power function, and the testing periods under each covariate are obtained. Case study indicates that the testing periods under each covariate obtained by the proposed method are more accurate than those obtained by the others, when the same criterion and confidence level 1-α are set.

In the area of prognostics and health management, data-driven methods increasingly show the superiority against model-based method due to the complex relationships and learn trends available in the data captured without the need for specific failure models. This paper uses Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) to build a model for non-stationary time series prediction. And it proves that the EMD-SVR method can solve the problem of few training samples in modeling the path of performance degradation. Then when the threshold is given, we can forecast the lifetime of engineering systems based on the performance degradation data.

This paper describes reliability assessment from measured data of double-stress accelerated degradation test which taking functional unit as research object. Most of performance degradation reliability analyses pay attention to components. Thereby assessments are relatively easy because they have simple function and clear failure mechanism. However, in reality, functional units compose system, and the reliability of a functional unit is affected by all of the jointing components synthetically which puts up a unitary degradation process. On this consideration, double-stress uniformly orthogonal accelerated test is putting on the whole board of functional units under temperature and relative humidity stresses (80 °C,55%), (100 °C,25%), (90 °C,85%), (110 °C,65%). Accelerated test design is introduced in this paper and the collected measured degradation data are shown by figures. According to statistical analysis reliability information is inferred from test data and assessment result is compared to results educed by other methods. Conclusion is result gain by this present way and is basically consistent with other studies. Functional unit reliability assessment based on performance degradation data is feasible and reliable.

This paper measures the twenty one geometric errors of numerical control machining center with parameter identification through laser interferometer, the main contents illustrate the measurement system, measurement model and some testing results combined with specific experimental conditions, at the same time provide particular reference value on numerical control machine tool(NC machine tool) geometric precision detection.

According to the required interval estimation precision of mean time between failures (MTBF) and reliability for numerical control (NC) machine tools, where failure processes can be described by the power law process model, the minimal testing truncated time and sample size of the reliability assessment test are given using Fisher information matrix method. The results show that there are two main factors which affect the truncated time and sample size. They are the log ratio of upper bound to lower bound and confidence of interval estimation for reliability indices, respectively. Among them, the minimal sample size increases significantly as the improvement of required precision of interval estimation for reliability indices and confidence increases, but the minimal testing truncated time is less affected by these two factors.