# Reliability Evaluation of NC Machine Tools considering Working Conditions

Authors: Hongzhou Li; Zhaojun Yang; Binbin Xu; Chuanhai Chen; Yingnan Kan; Guofei Liu
Journal: Mathematical Problems in Engineering
 (2016)

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## Abstract

Reliability evaluation is the basis for reliability design of NC machine tools. Since traditional reliability evaluation methods do not consider the working conditions’ effects on reliability, there is a great error of a result of a traditional method compared with an actual value. A new reliability evaluation model of NC machine tools is proposed based on the Cox proportional hazards model, which describes the mathematical relation between the working condition covariates and the reliability level of NC machine tools. Firstly, the coefficients of working condition covariates in the new reliability evaluation model are estimated by the partial likelihood estimation method; secondly, the working condition covariates which have no effects on the reliability of NC machine tools are eliminated by the likelihood ratio test; then parameters of the baseline failure rate function are estimated by the maximum likelihood estimation method. Thus, the reliability evaluation model of NC machine tool is obtained under different working conditions and the reliability level of NC machine tools is obtained. Case study shows that the proposed method could establish the relation between the working condition covariates and the reliability level of NC machine tools, and it would provide a new way for the reliability evaluation of NC machine tools.


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## Body

## 1. Introduction

With the rapid development of high-speed and high-precision technologies, NC machine tools are becoming the main equipment for advanced manufacturing technology [1], and so they have been applied widely in many industries and regions. Kinds of machining workpieces are also very different from user to user. Meanwhile, the atmosphere pressure and temperature vary greatly from one region to another. Therefore, NC machine tools are usually in different working conditions [2]. Generally, different working conditions have different impacts on reliability of products [3], which have been confirmed by many researches [4–10]. Hu studied the influence of various road conditions on reliability of cars, and the strengthening coefficients based on the strengthening road conditions were obtained [4]. The reliability model of power system considering weather conditions was established and the weather conditions were divided into 3-state or 2-state weather model [5, 6]. In order to evaluate the reliability of electronic system, hybrid stochastic Petri net was used to establish the reliability model. And the reliability levels of the system under different temperature and voltage were obtained [7]. Chen et al. studied the failure physics equation, which is generalized Eyring model, of aerospace electrical connectors; the multiple-stress accelerated test scheme is adopted; then the reliability levels of aerospace electrical connectors under different temperature and vibration stresses were obtained [8]. Besides, some scholars established relations between the environmental stresses and reliability levels of some products by carrying out accelerated life tests [11, 12]. Li et al. established the relationship between working conditions (the speed and load) and reliability of harmonic driver by accelerated life test [11]. Nogueira et al. studied temperature, humidity, and current impacts on reliability of high luminosity AlGaInP LEDs by accelerated life test [12]. Thus, it can be deduced from the above studies that working conditions would also affect the reliability of NC machine tools. Different working conditions cause different reliability levels, and the more different the working conditions are, the more obvious the disparity of the reliability levels is.Reliability is one of the most important indicators of measuring the performance of NC machine tools, which has always been the researching focus by scholars [13–17]. Given that the result of reliability evaluation is influenced by many factors, methods including the Markov model [18], Petri net [19], Monto Carlo method [20], and Bayesian method [21] are applied to reliability evaluation of NC machine tools. However, for the present, the relation between reliability of the NC machine tools and the working conditions has not been established, which causes error in the result of reliability evaluation of NC machine tools compared with the actual value. Therefore, to decrease the evaluation error, it is of great engineering significance to establish the relation between the reliability of NC machine tools and the working conditions, which is also a hard problem in reliability research on NC machine tools.In different research areas, there are many models to describe the relations between the reliability and the working conditions. Commonly seen models, besides [4–12], include Arrhenius Model [22], Inverse Power Law Model [23], and Cumulative Exposure Model [24], as well as other models [25]. However, each of them is usually aimed at a specific type of product. For the NC machine tool is a typical mechanic-electric-hydraulic system, the above methods are difficultly used to establish the relationship between working conditions and reliability.The proportional hazards model is a life statistical model, which is usually used for survival analysis in medicine field [26, 27], and can describe relation between patients and influencing factor. Thus, a new reliability evaluation method for NC machine tools based on the Cox proportional hazards model is proposed in this paper. Working conditions of NC machine tools are taken as covariates, and then the relation between the reliability level of NC machine tools and the covariates is established based on the proposed model. The coefficients of working condition covariates of the proposed model are estimated by the partial likelihood method; the covariates which have no effects on the reliability of NC machine tools are eliminated by the likelihood ratio test; the parameters of baseline failure rate function are estimated by the maximum likelihood estimation. Then, the reliability model of NC machine tools is developed under different working conditions. A batch of NC machine tools is taken as the research object for case study, where the impact laws of environment temperature, cutting fluid, number of tool changes, and cutting force on the reliability of NC machine tools are researched, respectively. Finally, the feasibility of the proposed method is validated in the case study.


## 2. Reliability Model of NC Machine Tools considering Working Conditions

### 2.1. Proportional Hazards Model

The proportional hazards model was developed in 1972 by Cox, a British statistician. This model considers the relation between failure rate and covariates, which is defined as [28](1)λt,X=λ0tgX,where t is the time between failures (TBF, a random variable) of NC machine tools, X=X1,X2,…,Xi,…,XnT is the vector of working condition covariates, which affects the failure rate of NC machine tools, Xi is the ith covariate, such as cutting force, environment temperature, number of tool changes, or vibration, and g(X) is function of working condition covariates.In general,g(X) can be expressed as(2)gX=exp⁡βX,where β=(β1,β2,…,βi,…,βn) is the vector of X’s coefficients, which reflect the covariates’ influences on the failure rate function, and βi is the coefficient of Xi. When βi>0, it indicates that Xi catalyzes the machine tools to fail; when βi=0, Xi has no effects on the failure rate of machine tools; when βi<0, Xi depresses the machine tools to fail. λ0(t) is the baseline failure rate of NC machine tools, that is, the failure rate function when X=0; λ(t,X) represents the failure rate function of NC machine tools under covariate X.Substituting (2) in (1) gets (3)λt,X=λ0texp⁡βX.Equation (3) is equivalent to the following equation:(4)λt,Xλ0t=exp⁡βX.The failure rate function of NC machine tools under the covariateX1 is λ(t,X1); thus, (5)λt,X1λ0t=exp⁡βX1.According to (4) and (5), then (6)λt,Xλt,X1=exp⁡βX-X1.Therefore,(7)λt,X=λt,X1exp⁡βX-X1.Assume that the probability density function (PDF) of NC machine tools’ TBF under covariate vectorX is f(t,X), and corresponding reliability function is R(t,X). According to (7), R(t,X) of NC machine tools is(8)Rt,X=Rt,X1expβX-X1,where R(t,X1) is the reliability function of NC machine tools under covariate vector X1.R
(
t
,
X
1
) can be expressed by(9)Rt,X1=exp⁡-∫-∞tλt,udu.TBF of NC machine tools is generally considered to follow two-parameter Weibull distribution [2, 13, 21, 29, 30]. Suppose that, under working condition covariate X1, the failure rate function can be expressed by(10)λ0t=mηtηm-1.Therefore, (7) is equivalent to(11)λt,X=mηtηm-1·exp⁡βX-X1,where m is the shape parameter, m>0; η is the scale parameter, η>0.So(12)Rt,X=exp⁡-tηmexpβX-X1.Therefore, the PDF of NC machine tools’ TBF considering the working conditions can be expressed as(13)ft,X=exp⁡-tηmexpβX-X1·mηtηm-1·expβX-X1.Then, MTBF of NC machine tools under covariate vectorX1 can be obtained by(14)MTBF=ηΓ1+1m,where Γ(∗) is Gamma function.


### 2.2. Parameter Estimation

There are several parameters and coefficients in (11), which are m and η and β=(β1,β2,…,βi,…,βn). Thus, a two-step estimation method to estimate these parameters and coefficients is employed. Firstly, β is estimated by the partial likelihood estimation method [31]; then, m and η are estimated by the maximum likelihood estimation method.


#### 2.2.1. Covariate Coefficients Estimation

From (7), we can get(15)λt,Xλt,X1=expβX-X1.Assuming that there aren failures, the jth failure of NC machine tools can be expressed as (tj, δj, Xj), where tj is time between the j-1th failure and the jth failure; δj is an indicator variable of datum tj; when δj=1, tj is noncensoring time and when δj=0, tj is censoring time.Equation (16) is k dimensional column vector, which indicates that there are k covariates in the jth failure of NC machine tools:(16)Xj=Xj1,Xj2,…,XjkT.Therefore, the partial likelihood function is given by(17)Lβ=∑j=1rexp⁡Sjβ∑l∈HtjexpXlβdj,where dj is the number of failures whose TBF is equal to tj. If Dj is the set of failures of NC machine tools at time tj, then dj=Dj. Sj is the sum of working condition covariates of dj failures, and so Sj=∑l∈DjXl when l∈Dj; H(tj) is the set of data that NC machine tools do not fail and there is no censoring at tj.Now take the logarithm of both sides in (17); then(18)ln⁡Lβ=∑j=1rSjβ-∑j=1rdjln⁡∑l∈Htjexp⁡Xlβ.Take the first derivative with respect toβ and let it be equal to zero, so(19)∂ln⁡Lβ∂βp=∑j=1rSjp-dj∑l∈HtjXlpexp⁡Xlβ∑l∈HtjexpXlβp=1,2,…,k,where Sjp is the pth element in Sj=(Sj1,Sj2,…,Sjk).There is no analytical solution to (19). So Newton-Raphson iterative method is applied to estimating the parameters [32].The second partial derivatives ofln⁡L(β) consist of the k×k order matrix I, of which the elements are(20)Ipqβ=∂2ln⁡Lβ∂βp·∂βq=∑j=1rdj∑l∈HtjXlpXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ-∑l∈HtjXlpexp⁡Xlβ·∑l∈HtjXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ2,where p,q=1,2,…,k.Soβ can be estimated by Newton-Raphson numerical algorithm.


#### 2.2.2. Elimination of No Impacting Covariate

In order to eliminate the covariates which have little or no impact on the reliability of NC machine tools, the likelihood ratio test is used and the procedure is shown in Figure1 [33].Figure 1
Procedure of the likelihood ratio test.The likelihood ratio statistics are constructed according to (13) and (17). Suppose that there are k covariates in the model and -2ln⁡[L(β^)/L(β^i)] is approximate Chi-square distribution whose degree of freedom is k-1. Lβ^ is the partial likelihood function of the model which does not contain covariate coefficient βi, and Lβ^i is partial likelihood function of the model which includes covariate coefficient βi. So(21)λ2k-1=-2ln⁡Lβ^Lβ^i.For a given confidence levelα, if λ2k-1>λα2(k-1), then βi has obvious effects on the model and it should be considered in the model; if λ2(k-1)<λα2(k-1), then βi has no effects on the model and it should not be considered.


#### 2.2.3. Parameters Estimation of Baseline Failure Function

The maximum likelihood estimation method is adopted to estimate the parametersm and η in (13), and the likelihood function is given as(22)Lη,m=∏i=1rfti,Xi∏i=r+1nRti,Xi=∏i=1rexp⁡-tiηmexpβXi-X1·mηtiηm-1·exp⁡βXi-X1·∏i=r+1nexp⁡-tiηmexpβXi-X1.Take the logarithm of both sides in (22); then(23)LnLη,m=rln⁡m-rmln⁡η+m-1ln⁡t1t2⋯tr-t1ηm+t2ηm+⋯+trηm+βX1+X2+⋯+Xr-rX1+∑i=r+1n-tiηmexp⁡βXi-X1.Take the partial derivative of the parametersη and m in (23), respectively, and then(24)∂LnLη,m∂η=-rmη+mt1mη-m-1+mt2mη-m-1+⋯+mtrmη-m-1+∑i=r+1nmtimη-m-1exp⁡βXi-X1,∂LnLη,m∂m=rm-rln⁡η+lnt1t2⋯tr-t1ηmln⁡t1η+t2ηmln⁡t2η+⋯+trηmln⁡trη+∑i=r+1n-tiηmln⁡tiηexp⁡βXi-X1.Since (24) have no analytical solutions, Newton-Raphson numerical algorithm is used to estimate parameters η and m.


## 2.1. Proportional Hazards Model

The proportional hazards model was developed in 1972 by Cox, a British statistician. This model considers the relation between failure rate and covariates, which is defined as [28](1)λt,X=λ0tgX,where t is the time between failures (TBF, a random variable) of NC machine tools, X=X1,X2,…,Xi,…,XnT is the vector of working condition covariates, which affects the failure rate of NC machine tools, Xi is the ith covariate, such as cutting force, environment temperature, number of tool changes, or vibration, and g(X) is function of working condition covariates.In general,g(X) can be expressed as(2)gX=exp⁡βX,where β=(β1,β2,…,βi,…,βn) is the vector of X’s coefficients, which reflect the covariates’ influences on the failure rate function, and βi is the coefficient of Xi. When βi>0, it indicates that Xi catalyzes the machine tools to fail; when βi=0, Xi has no effects on the failure rate of machine tools; when βi<0, Xi depresses the machine tools to fail. λ0(t) is the baseline failure rate of NC machine tools, that is, the failure rate function when X=0; λ(t,X) represents the failure rate function of NC machine tools under covariate X.Substituting (2) in (1) gets (3)λt,X=λ0texp⁡βX.Equation (3) is equivalent to the following equation:(4)λt,Xλ0t=exp⁡βX.The failure rate function of NC machine tools under the covariateX1 is λ(t,X1); thus, (5)λt,X1λ0t=exp⁡βX1.According to (4) and (5), then (6)λt,Xλt,X1=exp⁡βX-X1.Therefore,(7)λt,X=λt,X1exp⁡βX-X1.Assume that the probability density function (PDF) of NC machine tools’ TBF under covariate vectorX is f(t,X), and corresponding reliability function is R(t,X). According to (7), R(t,X) of NC machine tools is(8)Rt,X=Rt,X1expβX-X1,where R(t,X1) is the reliability function of NC machine tools under covariate vector X1.R
(
t
,
X
1
) can be expressed by(9)Rt,X1=exp⁡-∫-∞tλt,udu.TBF of NC machine tools is generally considered to follow two-parameter Weibull distribution [2, 13, 21, 29, 30]. Suppose that, under working condition covariate X1, the failure rate function can be expressed by(10)λ0t=mηtηm-1.Therefore, (7) is equivalent to(11)λt,X=mηtηm-1·exp⁡βX-X1,where m is the shape parameter, m>0; η is the scale parameter, η>0.So(12)Rt,X=exp⁡-tηmexpβX-X1.Therefore, the PDF of NC machine tools’ TBF considering the working conditions can be expressed as(13)ft,X=exp⁡-tηmexpβX-X1·mηtηm-1·expβX-X1.Then, MTBF of NC machine tools under covariate vectorX1 can be obtained by(14)MTBF=ηΓ1+1m,where Γ(∗) is Gamma function.


## 2.2. Parameter Estimation

There are several parameters and coefficients in (11), which are m and η and β=(β1,β2,…,βi,…,βn). Thus, a two-step estimation method to estimate these parameters and coefficients is employed. Firstly, β is estimated by the partial likelihood estimation method [31]; then, m and η are estimated by the maximum likelihood estimation method.


### 2.2.1. Covariate Coefficients Estimation

From (7), we can get(15)λt,Xλt,X1=expβX-X1.Assuming that there aren failures, the jth failure of NC machine tools can be expressed as (tj, δj, Xj), where tj is time between the j-1th failure and the jth failure; δj is an indicator variable of datum tj; when δj=1, tj is noncensoring time and when δj=0, tj is censoring time.Equation (16) is k dimensional column vector, which indicates that there are k covariates in the jth failure of NC machine tools:(16)Xj=Xj1,Xj2,…,XjkT.Therefore, the partial likelihood function is given by(17)Lβ=∑j=1rexp⁡Sjβ∑l∈HtjexpXlβdj,where dj is the number of failures whose TBF is equal to tj. If Dj is the set of failures of NC machine tools at time tj, then dj=Dj. Sj is the sum of working condition covariates of dj failures, and so Sj=∑l∈DjXl when l∈Dj; H(tj) is the set of data that NC machine tools do not fail and there is no censoring at tj.Now take the logarithm of both sides in (17); then(18)ln⁡Lβ=∑j=1rSjβ-∑j=1rdjln⁡∑l∈Htjexp⁡Xlβ.Take the first derivative with respect toβ and let it be equal to zero, so(19)∂ln⁡Lβ∂βp=∑j=1rSjp-dj∑l∈HtjXlpexp⁡Xlβ∑l∈HtjexpXlβp=1,2,…,k,where Sjp is the pth element in Sj=(Sj1,Sj2,…,Sjk).There is no analytical solution to (19). So Newton-Raphson iterative method is applied to estimating the parameters [32].The second partial derivatives ofln⁡L(β) consist of the k×k order matrix I, of which the elements are(20)Ipqβ=∂2ln⁡Lβ∂βp·∂βq=∑j=1rdj∑l∈HtjXlpXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ-∑l∈HtjXlpexp⁡Xlβ·∑l∈HtjXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ2,where p,q=1,2,…,k.Soβ can be estimated by Newton-Raphson numerical algorithm.


### 2.2.2. Elimination of No Impacting Covariate

In order to eliminate the covariates which have little or no impact on the reliability of NC machine tools, the likelihood ratio test is used and the procedure is shown in Figure1 [33].Figure 1
Procedure of the likelihood ratio test.The likelihood ratio statistics are constructed according to (13) and (17). Suppose that there are k covariates in the model and -2ln⁡[L(β^)/L(β^i)] is approximate Chi-square distribution whose degree of freedom is k-1. Lβ^ is the partial likelihood function of the model which does not contain covariate coefficient βi, and Lβ^i is partial likelihood function of the model which includes covariate coefficient βi. So(21)λ2k-1=-2ln⁡Lβ^Lβ^i.For a given confidence levelα, if λ2k-1>λα2(k-1), then βi has obvious effects on the model and it should be considered in the model; if λ2(k-1)<λα2(k-1), then βi has no effects on the model and it should not be considered.


### 2.2.3. Parameters Estimation of Baseline Failure Function

The maximum likelihood estimation method is adopted to estimate the parametersm and η in (13), and the likelihood function is given as(22)Lη,m=∏i=1rfti,Xi∏i=r+1nRti,Xi=∏i=1rexp⁡-tiηmexpβXi-X1·mηtiηm-1·exp⁡βXi-X1·∏i=r+1nexp⁡-tiηmexpβXi-X1.Take the logarithm of both sides in (22); then(23)LnLη,m=rln⁡m-rmln⁡η+m-1ln⁡t1t2⋯tr-t1ηm+t2ηm+⋯+trηm+βX1+X2+⋯+Xr-rX1+∑i=r+1n-tiηmexp⁡βXi-X1.Take the partial derivative of the parametersη and m in (23), respectively, and then(24)∂LnLη,m∂η=-rmη+mt1mη-m-1+mt2mη-m-1+⋯+mtrmη-m-1+∑i=r+1nmtimη-m-1exp⁡βXi-X1,∂LnLη,m∂m=rm-rln⁡η+lnt1t2⋯tr-t1ηmln⁡t1η+t2ηmln⁡t2η+⋯+trηmln⁡trη+∑i=r+1n-tiηmln⁡tiηexp⁡βXi-X1.Since (24) have no analytical solutions, Newton-Raphson numerical algorithm is used to estimate parameters η and m.


## 2.2.1. Covariate Coefficients Estimation

From (7), we can get(15)λt,Xλt,X1=expβX-X1.Assuming that there aren failures, the jth failure of NC machine tools can be expressed as (tj, δj, Xj), where tj is time between the j-1th failure and the jth failure; δj is an indicator variable of datum tj; when δj=1, tj is noncensoring time and when δj=0, tj is censoring time.Equation (16) is k dimensional column vector, which indicates that there are k covariates in the jth failure of NC machine tools:(16)Xj=Xj1,Xj2,…,XjkT.Therefore, the partial likelihood function is given by(17)Lβ=∑j=1rexp⁡Sjβ∑l∈HtjexpXlβdj,where dj is the number of failures whose TBF is equal to tj. If Dj is the set of failures of NC machine tools at time tj, then dj=Dj. Sj is the sum of working condition covariates of dj failures, and so Sj=∑l∈DjXl when l∈Dj; H(tj) is the set of data that NC machine tools do not fail and there is no censoring at tj.Now take the logarithm of both sides in (17); then(18)ln⁡Lβ=∑j=1rSjβ-∑j=1rdjln⁡∑l∈Htjexp⁡Xlβ.Take the first derivative with respect toβ and let it be equal to zero, so(19)∂ln⁡Lβ∂βp=∑j=1rSjp-dj∑l∈HtjXlpexp⁡Xlβ∑l∈HtjexpXlβp=1,2,…,k,where Sjp is the pth element in Sj=(Sj1,Sj2,…,Sjk).There is no analytical solution to (19). So Newton-Raphson iterative method is applied to estimating the parameters [32].The second partial derivatives ofln⁡L(β) consist of the k×k order matrix I, of which the elements are(20)Ipqβ=∂2ln⁡Lβ∂βp·∂βq=∑j=1rdj∑l∈HtjXlpXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ-∑l∈HtjXlpexp⁡Xlβ·∑l∈HtjXlqexp⁡Xlβ∑l∈Htjexp⁡Xlβ2,where p,q=1,2,…,k.Soβ can be estimated by Newton-Raphson numerical algorithm.


## 2.2.2. Elimination of No Impacting Covariate

In order to eliminate the covariates which have little or no impact on the reliability of NC machine tools, the likelihood ratio test is used and the procedure is shown in Figure1 [33].Figure 1
Procedure of the likelihood ratio test.The likelihood ratio statistics are constructed according to (13) and (17). Suppose that there are k covariates in the model and -2ln⁡[L(β^)/L(β^i)] is approximate Chi-square distribution whose degree of freedom is k-1. Lβ^ is the partial likelihood function of the model which does not contain covariate coefficient βi, and Lβ^i is partial likelihood function of the model which includes covariate coefficient βi. So(21)λ2k-1=-2ln⁡Lβ^Lβ^i.For a given confidence levelα, if λ2k-1>λα2(k-1), then βi has obvious effects on the model and it should be considered in the model; if λ2(k-1)<λα2(k-1), then βi has no effects on the model and it should not be considered.


## 2.2.3. Parameters Estimation of Baseline Failure Function

The maximum likelihood estimation method is adopted to estimate the parametersm and η in (13), and the likelihood function is given as(22)Lη,m=∏i=1rfti,Xi∏i=r+1nRti,Xi=∏i=1rexp⁡-tiηmexpβXi-X1·mηtiηm-1·exp⁡βXi-X1·∏i=r+1nexp⁡-tiηmexpβXi-X1.Take the logarithm of both sides in (22); then(23)LnLη,m=rln⁡m-rmln⁡η+m-1ln⁡t1t2⋯tr-t1ηm+t2ηm+⋯+trηm+βX1+X2+⋯+Xr-rX1+∑i=r+1n-tiηmexp⁡βXi-X1.Take the partial derivative of the parametersη and m in (23), respectively, and then(24)∂LnLη,m∂η=-rmη+mt1mη-m-1+mt2mη-m-1+⋯+mtrmη-m-1+∑i=r+1nmtimη-m-1exp⁡βXi-X1,∂LnLη,m∂m=rm-rln⁡η+lnt1t2⋯tr-t1ηmln⁡t1η+t2ηmln⁡t2η+⋯+trηmln⁡trη+∑i=r+1n-tiηmln⁡tiηexp⁡βXi-X1.Since (24) have no analytical solutions, Newton-Raphson numerical algorithm is used to estimate parameters η and m.


## 3. Case Study

### 3.1. Field Test Data Collection

The failure data and the corresponding load data analyzed in this study were collected from 19 machining centers which were used in two user enterprises located in southwest of China. These machining centers were traced from February 10, 2014, to June 20, 2014. The failure data includes failure happening time and ending time, failure cause, failure position, failure phenomenon, and the repair information, all of which are recorded in AppendixA by testers. The load data, including manufacturing procedure, cutting parameters, material, cutting fluid, and environment temperature, are all recorded in Appendix B by testers.According to the failure data and the corresponding load data recorded in AppendixA and B, TBFs and corresponding cutting force, environment temperature, cutting fluid condition, number of tool changes, and other working conditions of the traced machining centers are elicited and arranged. Cutting force, number of tool changes, environmental temperature, and cutting fluid are chosen as the working condition covariates considering the limitation of the testing conditions. The environmental temperature, cutting fluid, and the number of tool changes can be found in Appendix B directly. As for the field reliability test, it is impossible to install force sensors on machine tools, and thus cutting force cannot be measured by the force sensor directly. Therefore, the average cutting force is calculated by empirical formula (25) [34] in combination with the data recorded in Appendix B:(25)Fc=CFapxFfzyFaeuFZd0qFnwF,where CF, xF, yF, uF, qF, and wF are coefficients which can be found in [34]; ap is the depth of cuts; fz is the feed of a tooth; ae is the cutting breadth; z is the number of milling cutter teeth; d0 is the diameter of milling cutter; n is the rotation speed of milling cutter.Machining centers used in the first enterprise are mainly for machining cylinder block, cylinder head, connecting plate, and the corresponding mould workpieces. The cutting times of cylinder block, cylinder head, and connecting plate account for 89 percent of the total cutting time. Generally, the machining process of workpiece consists of several operations or more; we calculate cutting forces of the above workpieces, respectively, by the parameters of the operation whose cutting time is the longest. And machining centers in the second enterprise are mainly for machining flywheel, flywheel housing, and cylinder. While the cutting time of these workpieces accounts for about 92 percent of the total cutting time, we also calculate cutting forces of the three workpieces, respectively, by the parameters of the operation whose cutting time is the longest. The parameters of the longest cutting time of the above workpieces are shown in Table1.Table 1
Cutting parameters of workpieces.
Workpiece name
Cutting tool name
Tool material
Tool type
Rotation speed/(r/min)
Depth ofcuts/(mm)
Feed of a tooth/(mm/z)
Cutting breadth/(mm)
Diameter of milling cutter/(mm)
Number of milling cutter teeth
Flywheel
φ10 alloy milling cutter
Cemented carbide
End milling cutter
500
3
0.011
10
10
4
Cylinder block
φ25R6 alloy milling cutter
Cemented carbide
Face milling cutter
1100
1.7
0.046
25
25
2
Cylinder head
φ12 alloy milling cutter
Cemented carbide
End milling cutter
400
6.3
0.18
0.5
12
4
Mould
φ25R6 alloy milling cutter
Cemented carbide
End milling cutter
500
1.8
0.165
15
25
2
Flywheel housing
φ50 milling cutter
Cemented carbide
Face milling cutter
400
2.28
0.028
48
50
5
Cylinder
φ40 milling cutter
Cemented carbide
Face milling cutter
500
3
0.04
36
40
3
Connecting plate
φ12 alloy milling cutter
Cemented carbide
End milling cutter
400
5.2
0.18
0.62
12
4The average cutting force for the seven workpieces is calculated according to Table1 and (25). The covariates, TBFs, and other data are shown in Table 2.Table 2
Failure data of NC machine tools under different working conditions covariates.
Workpiece name
Cutting force/KN
Number of tool changes/(N/h)
Cutting fluid
Temperature/°C
TBF/h
Data type
Flywheel
0.35
2
1
20
437
1
Flywheel
0.35
2
1
20
1896
1
Flywheel
0.35
2
1
20
340
1
Flywheel
0.35
2
1
20
244
1
Flywheel
0.35
2
1
20
249
1
Flywheel
0.35
2
1
20
898
1
Flywheel
0.35
2
1
20
1148
0
Cylinder block
0.43
17
0
21
158
1
Cylinder block
0.43
17
0
21
67
1
Cylinder block
0.43
17
0
21
242
1
Cylinder block
0.43
17
0
21
107
1
Cylinder block
0.43
17
0
21
155
1
Cylinder block
0.43
17
0
21
1717
1
Cylinder block
0.43
17
0
21
812
1
Cylinder block
0.43
17
0
21
724
1
Cylinder head
0.54
4
0
22
316
1
Cylinder head
0.54
4
0
22
99
1
Cylinder head
0.54
4
0
22
1419
1
Cylinder head
0.54
4
0
22
1430
1
Cylinder head
0.54
4
0
22
225
1
Cylinder head
0.54
4
0
22
773
1
Cylinder head
0.54
4
0
22
843
0
Mould
0.78
10
1
19
398
1
Mould
0.78
10
1
19
29
1
Mould
0.78
10
1
19
401
1
Mould
0.78
10
1
19
1148
1
Mould
0.78
10
1
19
1012
1
Mould
0.78
10
1
19
733
1
Mould
0.78
10
1
19
1717
1
Mould
0.78
10
1
19
773
1
Mould
0.78
10
1
19
445
0
Flywheel housing
0.81
4
1
20
348
1
Flywheel housing
0.81
4
1
20
167
1
Flywheel housing
0.81
4
1
20
1232
1
Flywheel housing
0.81
4
1
20
1118
1
Flywheel housing
0.81
4
1
20
633
1
Flywheel housing
0.81
4
1
20
382
1
Flywheel housing
0.81
4
1
20
321
1
Flywheel housing
0.81
4
1
20
576
0
Cylinder
0.84
6
0
19
58
1
Cylinder
0.84
6
0
19
37
1
Cylinder
0.84
6
0
19
58
1
Cylinder
0.84
6
0
19
592
1
Cylinder
0.84
6
0
19
1008
1
Cylinder
0.84
6
0
19
365
1
Cylinder
0.84
6
0
20
1144
1
Cylinder
0.84
6
0
20
1430
1
Cylinder
0.84
6
0
20
373
1
Cylinder
0.84
6
0
20
659
1
Connecting plate
1.03
14
1
22
234
1
Connecting plate
1.03
14
1
22
175
1
Connecting plate
1.03
14
1
22
190
1
Connecting plate
1.03
14
1
22
151
1
Connecting plate
1.03
14
1
22
18
1
Connecting plate
1.03
14
1
22
530
1
Connecting plate
1.03
14
1
22
349
1
Connecting plate
1.03
14
1
22
526
1
Connecting plate
1.03
14
1
22
368
1
Connecting plate
1.03
14
1
22
174
1


### 3.2. Model Establishment and Comparison between the Traditional Method and the New Method

The coefficientβ of the covariate in the proportional hazards model is calculated by the partial likelihood estimation method and the likelihood ratio test, where α=0.05. The results are shown in Table 3.Table 3
Computation by the partial likelihood estimation method and the likelihood ratio test.
Step
k
Working conditions covariates
λ
2
(
k
)
λ
0.05
2
(
k
)
β
1
β
2
β
3
β
4
1
4
1.285
0.040
−0.065
0.1340
7.871
9.488
2
3
1.226
0.040
0.1410
7.809
7.815
3
2
1.142
0.049
6.552
5.991It is seen from Table3 that λ2(k) is greater than λ0.052(k) in the third step and there are only two covariates in the model, cutting force and number of tool changes, in the third step. Thus, the working condition covariates of cutting fluid and temperature (19°C–22°C) have no effects on the reliability of NC machine tools.The failure rate function with the following working condition covariates, cutting forceFc=0.35 KN and number of tool changes Nh=2, is chosen as the baseline failure rate function in (15). Then m=1.2121 and η=1156 in (15) are estimated by the maximum likelihood estimation method. The reliability models under different covariates are obtained and shown in Table 4.Table 4
Reliability models of NC machine tools under different working condition covariates.
Working condition covariates
Proportional hazards functionλ(t/X)
F
c/KN
N
h/(n/h)
0.35
2
1.2121
1156
t
1156
0.2121
0.43
17
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.08
+
0.049
∗
15
0.54
4
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.19
+
0.049
∗
2
0.78
10
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.43
+
0.049
∗
8
0.81
4
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.46
+
0.049
∗
2
0.84
6
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.49
+
0.049
∗
4
1.03
14
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.68
+
0.049
∗
12The proportional hazard models under different working condition covariates shown in Table4 can be transformed into Weibull failure rate models, and then MTBF can be obtained by (14), as shown in Table 5.Table 5
Comparison between the traditional method and the new method.
Method
Working condition covariates
λ
(
t
,
X
)
MTBF/h
MTBF comparison (MC)
F
c/KN
N
h (n/h)
0.35
2
1.2121
1156
t
1156
0.2121
1084.6
1.8540
0.43
17
1.2121
584.5916
t
584.5916
0.2121
548.5002
0.9376
0.54
4
1.2121
891.4274
t
891.4274
0.2121
836.4116
1.4298
The new method
0.78
10
1.2121
557.8750
t
557.8750
0.2121
523.4331
0.8948
0.81
4
1.2121
691.2005
t
691.2005
0.2121
648.5274
1.1086
0.84
6
1.2121
619.7461
t
619.7461
0.2121
581.4844
0.994
1.03
14
1.2121
374.9816
t
374.9816
0.2121
351.8384
0.6014
The traditional method
1.1468
614.1904
t
614.1904
1.1468
585.0025
1For comparison, reliability model parameters and MTBF of NC machine tools obtained by the traditional method [35], which does not consider the working condition covariates and is based on two-parameter Weibull distribution, are also shown in Table 5. The traditional method uses only two columns (TBF and data type) of data in Table 2 of the same batch of NC machine tools. The detailed procedure of calculation of the traditional method is given in [35], and the corresponding evaluation result is shown in the last row of Table 5.For an obvious comparison, the last column of Table5 gives the MTBF comparison (MC), which is obtained by(26)MC=MTBFnewMTBFtrad,where MTBFtrad is the MTBF obtained by the traditional method; MTBFnew is the MTBF under different working condition covariates obtained by the new method.From Table5, it is seen that the traditional method gives MTBFtrad=585.0025 h, and the MTBFnew given by the new method varies with different working conditions of NC machine tools. For example, firstly, when the cutting force Fc=1.03 KN and the number of tool changes Nh=14, MTBFnew is 351.8384 h, which is only 0.6014 times of MTBFtrad. Secondly, the reliability level MTBFnew could reach 1084.6 h when the cutting force and the number of tool changes are getting smaller (Fc=0.35 KN and Nh=2), which is 1.8540 times of MTBFtrad. Thirdly, when Nh=4, and Fc changes from 0.54 KN to 0.81 KN, the values of MTBFnew change from 1.4298∗MTBFtrad to 1.1086∗MTBFtrad. And when the cutting force Fc=0.84 KN and the number of tool changes Nh=6, MTBFnew is 581.4844 h, which is 0.994 times of MTBFtrad. From the above analysis it can be seen that MTBFnew obtained by the new method increases as the cutting force or the number of tool changes increases. Only under some particular working conditions does the MTBFtrad obtained by the traditional method equal MTBFnew.From Table5, the PDFs and cumulative distribution functions (CDFs) of NC machine tools’ TBF under different working condition covariates obtained by the new method are shown in Figures 2 and 3, respectively. Meanwhile, the PDF and CDF obtained by the traditional method are also shown in Figures 2 and 3.Figure 2
PDFs obtained by the new method and the traditional method, respectively.Figure 3
CDFs obtained by the new method and the traditional method, respectively.It is seen from Figures2 and 3 that there are obvious distinctions between the PDF curves and CDF curves obtained by the traditional method and the new methods. The PDF curve and the CDF curve obtained by the traditional method are only one single curve, and only under some particular working conditions are they close to the PDF curves and CDF curves obtained by the new method. When the values of cutting force or the number of tool changes are comparatively large (e.g., the cutting force Fc = 1.03 KN and the number of tool changes Nh = 14), the PDF curves and the CDF curves are steep. And when the values of cutting force or the number of tool changes are comparatively small (e.g., Fc = 0.35 KN and Nh = 2), the PDF curves and the CDF curves are even. It can be concluded from the above facts that, under different working conditions, the failure rate of NC machine tools is different, and the traditional method does not consider the differences in the results of reliability evaluation caused by different working conditions.Therefore, the working conditions must be considered in evaluating the reliability level of NC machine tools.


## 3.1. Field Test Data Collection

The failure data and the corresponding load data analyzed in this study were collected from 19 machining centers which were used in two user enterprises located in southwest of China. These machining centers were traced from February 10, 2014, to June 20, 2014. The failure data includes failure happening time and ending time, failure cause, failure position, failure phenomenon, and the repair information, all of which are recorded in AppendixA by testers. The load data, including manufacturing procedure, cutting parameters, material, cutting fluid, and environment temperature, are all recorded in Appendix B by testers.According to the failure data and the corresponding load data recorded in AppendixA and B, TBFs and corresponding cutting force, environment temperature, cutting fluid condition, number of tool changes, and other working conditions of the traced machining centers are elicited and arranged. Cutting force, number of tool changes, environmental temperature, and cutting fluid are chosen as the working condition covariates considering the limitation of the testing conditions. The environmental temperature, cutting fluid, and the number of tool changes can be found in Appendix B directly. As for the field reliability test, it is impossible to install force sensors on machine tools, and thus cutting force cannot be measured by the force sensor directly. Therefore, the average cutting force is calculated by empirical formula (25) [34] in combination with the data recorded in Appendix B:(25)Fc=CFapxFfzyFaeuFZd0qFnwF,where CF, xF, yF, uF, qF, and wF are coefficients which can be found in [34]; ap is the depth of cuts; fz is the feed of a tooth; ae is the cutting breadth; z is the number of milling cutter teeth; d0 is the diameter of milling cutter; n is the rotation speed of milling cutter.Machining centers used in the first enterprise are mainly for machining cylinder block, cylinder head, connecting plate, and the corresponding mould workpieces. The cutting times of cylinder block, cylinder head, and connecting plate account for 89 percent of the total cutting time. Generally, the machining process of workpiece consists of several operations or more; we calculate cutting forces of the above workpieces, respectively, by the parameters of the operation whose cutting time is the longest. And machining centers in the second enterprise are mainly for machining flywheel, flywheel housing, and cylinder. While the cutting time of these workpieces accounts for about 92 percent of the total cutting time, we also calculate cutting forces of the three workpieces, respectively, by the parameters of the operation whose cutting time is the longest. The parameters of the longest cutting time of the above workpieces are shown in Table1.Table 1
Cutting parameters of workpieces.
Workpiece name
Cutting tool name
Tool material
Tool type
Rotation speed/(r/min)
Depth ofcuts/(mm)
Feed of a tooth/(mm/z)
Cutting breadth/(mm)
Diameter of milling cutter/(mm)
Number of milling cutter teeth
Flywheel
φ10 alloy milling cutter
Cemented carbide
End milling cutter
500
3
0.011
10
10
4
Cylinder block
φ25R6 alloy milling cutter
Cemented carbide
Face milling cutter
1100
1.7
0.046
25
25
2
Cylinder head
φ12 alloy milling cutter
Cemented carbide
End milling cutter
400
6.3
0.18
0.5
12
4
Mould
φ25R6 alloy milling cutter
Cemented carbide
End milling cutter
500
1.8
0.165
15
25
2
Flywheel housing
φ50 milling cutter
Cemented carbide
Face milling cutter
400
2.28
0.028
48
50
5
Cylinder
φ40 milling cutter
Cemented carbide
Face milling cutter
500
3
0.04
36
40
3
Connecting plate
φ12 alloy milling cutter
Cemented carbide
End milling cutter
400
5.2
0.18
0.62
12
4The average cutting force for the seven workpieces is calculated according to Table1 and (25). The covariates, TBFs, and other data are shown in Table 2.Table 2
Failure data of NC machine tools under different working conditions covariates.
Workpiece name
Cutting force/KN
Number of tool changes/(N/h)
Cutting fluid
Temperature/°C
TBF/h
Data type
Flywheel
0.35
2
1
20
437
1
Flywheel
0.35
2
1
20
1896
1
Flywheel
0.35
2
1
20
340
1
Flywheel
0.35
2
1
20
244
1
Flywheel
0.35
2
1
20
249
1
Flywheel
0.35
2
1
20
898
1
Flywheel
0.35
2
1
20
1148
0
Cylinder block
0.43
17
0
21
158
1
Cylinder block
0.43
17
0
21
67
1
Cylinder block
0.43
17
0
21
242
1
Cylinder block
0.43
17
0
21
107
1
Cylinder block
0.43
17
0
21
155
1
Cylinder block
0.43
17
0
21
1717
1
Cylinder block
0.43
17
0
21
812
1
Cylinder block
0.43
17
0
21
724
1
Cylinder head
0.54
4
0
22
316
1
Cylinder head
0.54
4
0
22
99
1
Cylinder head
0.54
4
0
22
1419
1
Cylinder head
0.54
4
0
22
1430
1
Cylinder head
0.54
4
0
22
225
1
Cylinder head
0.54
4
0
22
773
1
Cylinder head
0.54
4
0
22
843
0
Mould
0.78
10
1
19
398
1
Mould
0.78
10
1
19
29
1
Mould
0.78
10
1
19
401
1
Mould
0.78
10
1
19
1148
1
Mould
0.78
10
1
19
1012
1
Mould
0.78
10
1
19
733
1
Mould
0.78
10
1
19
1717
1
Mould
0.78
10
1
19
773
1
Mould
0.78
10
1
19
445
0
Flywheel housing
0.81
4
1
20
348
1
Flywheel housing
0.81
4
1
20
167
1
Flywheel housing
0.81
4
1
20
1232
1
Flywheel housing
0.81
4
1
20
1118
1
Flywheel housing
0.81
4
1
20
633
1
Flywheel housing
0.81
4
1
20
382
1
Flywheel housing
0.81
4
1
20
321
1
Flywheel housing
0.81
4
1
20
576
0
Cylinder
0.84
6
0
19
58
1
Cylinder
0.84
6
0
19
37
1
Cylinder
0.84
6
0
19
58
1
Cylinder
0.84
6
0
19
592
1
Cylinder
0.84
6
0
19
1008
1
Cylinder
0.84
6
0
19
365
1
Cylinder
0.84
6
0
20
1144
1
Cylinder
0.84
6
0
20
1430
1
Cylinder
0.84
6
0
20
373
1
Cylinder
0.84
6
0
20
659
1
Connecting plate
1.03
14
1
22
234
1
Connecting plate
1.03
14
1
22
175
1
Connecting plate
1.03
14
1
22
190
1
Connecting plate
1.03
14
1
22
151
1
Connecting plate
1.03
14
1
22
18
1
Connecting plate
1.03
14
1
22
530
1
Connecting plate
1.03
14
1
22
349
1
Connecting plate
1.03
14
1
22
526
1
Connecting plate
1.03
14
1
22
368
1
Connecting plate
1.03
14
1
22
174
1


## 3.2. Model Establishment and Comparison between the Traditional Method and the New Method

The coefficientβ of the covariate in the proportional hazards model is calculated by the partial likelihood estimation method and the likelihood ratio test, where α=0.05. The results are shown in Table 3.Table 3
Computation by the partial likelihood estimation method and the likelihood ratio test.
Step
k
Working conditions covariates
λ
2
(
k
)
λ
0.05
2
(
k
)
β
1
β
2
β
3
β
4
1
4
1.285
0.040
−0.065
0.1340
7.871
9.488
2
3
1.226
0.040
0.1410
7.809
7.815
3
2
1.142
0.049
6.552
5.991It is seen from Table3 that λ2(k) is greater than λ0.052(k) in the third step and there are only two covariates in the model, cutting force and number of tool changes, in the third step. Thus, the working condition covariates of cutting fluid and temperature (19°C–22°C) have no effects on the reliability of NC machine tools.The failure rate function with the following working condition covariates, cutting forceFc=0.35 KN and number of tool changes Nh=2, is chosen as the baseline failure rate function in (15). Then m=1.2121 and η=1156 in (15) are estimated by the maximum likelihood estimation method. The reliability models under different covariates are obtained and shown in Table 4.Table 4
Reliability models of NC machine tools under different working condition covariates.
Working condition covariates
Proportional hazards functionλ(t/X)
F
c/KN
N
h/(n/h)
0.35
2
1.2121
1156
t
1156
0.2121
0.43
17
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.08
+
0.049
∗
15
0.54
4
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.19
+
0.049
∗
2
0.78
10
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.43
+
0.049
∗
8
0.81
4
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.46
+
0.049
∗
2
0.84
6
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.49
+
0.049
∗
4
1.03
14
1.2121
1156
t
1156
0.2121
·
exp
⁡
1.142
∗
0.68
+
0.049
∗
12The proportional hazard models under different working condition covariates shown in Table4 can be transformed into Weibull failure rate models, and then MTBF can be obtained by (14), as shown in Table 5.Table 5
Comparison between the traditional method and the new method.
Method
Working condition covariates
λ
(
t
,
X
)
MTBF/h
MTBF comparison (MC)
F
c/KN
N
h (n/h)
0.35
2
1.2121
1156
t
1156
0.2121
1084.6
1.8540
0.43
17
1.2121
584.5916
t
584.5916
0.2121
548.5002
0.9376
0.54
4
1.2121
891.4274
t
891.4274
0.2121
836.4116
1.4298
The new method
0.78
10
1.2121
557.8750
t
557.8750
0.2121
523.4331
0.8948
0.81
4
1.2121
691.2005
t
691.2005
0.2121
648.5274
1.1086
0.84
6
1.2121
619.7461
t
619.7461
0.2121
581.4844
0.994
1.03
14
1.2121
374.9816
t
374.9816
0.2121
351.8384
0.6014
The traditional method
1.1468
614.1904
t
614.1904
1.1468
585.0025
1For comparison, reliability model parameters and MTBF of NC machine tools obtained by the traditional method [35], which does not consider the working condition covariates and is based on two-parameter Weibull distribution, are also shown in Table 5. The traditional method uses only two columns (TBF and data type) of data in Table 2 of the same batch of NC machine tools. The detailed procedure of calculation of the traditional method is given in [35], and the corresponding evaluation result is shown in the last row of Table 5.For an obvious comparison, the last column of Table5 gives the MTBF comparison (MC), which is obtained by(26)MC=MTBFnewMTBFtrad,where MTBFtrad is the MTBF obtained by the traditional method; MTBFnew is the MTBF under different working condition covariates obtained by the new method.From Table5, it is seen that the traditional method gives MTBFtrad=585.0025 h, and the MTBFnew given by the new method varies with different working conditions of NC machine tools. For example, firstly, when the cutting force Fc=1.03 KN and the number of tool changes Nh=14, MTBFnew is 351.8384 h, which is only 0.6014 times of MTBFtrad. Secondly, the reliability level MTBFnew could reach 1084.6 h when the cutting force and the number of tool changes are getting smaller (Fc=0.35 KN and Nh=2), which is 1.8540 times of MTBFtrad. Thirdly, when Nh=4, and Fc changes from 0.54 KN to 0.81 KN, the values of MTBFnew change from 1.4298∗MTBFtrad to 1.1086∗MTBFtrad. And when the cutting force Fc=0.84 KN and the number of tool changes Nh=6, MTBFnew is 581.4844 h, which is 0.994 times of MTBFtrad. From the above analysis it can be seen that MTBFnew obtained by the new method increases as the cutting force or the number of tool changes increases. Only under some particular working conditions does the MTBFtrad obtained by the traditional method equal MTBFnew.From Table5, the PDFs and cumulative distribution functions (CDFs) of NC machine tools’ TBF under different working condition covariates obtained by the new method are shown in Figures 2 and 3, respectively. Meanwhile, the PDF and CDF obtained by the traditional method are also shown in Figures 2 and 3.Figure 2
PDFs obtained by the new method and the traditional method, respectively.Figure 3
CDFs obtained by the new method and the traditional method, respectively.It is seen from Figures2 and 3 that there are obvious distinctions between the PDF curves and CDF curves obtained by the traditional method and the new methods. The PDF curve and the CDF curve obtained by the traditional method are only one single curve, and only under some particular working conditions are they close to the PDF curves and CDF curves obtained by the new method. When the values of cutting force or the number of tool changes are comparatively large (e.g., the cutting force Fc = 1.03 KN and the number of tool changes Nh = 14), the PDF curves and the CDF curves are steep. And when the values of cutting force or the number of tool changes are comparatively small (e.g., Fc = 0.35 KN and Nh = 2), the PDF curves and the CDF curves are even. It can be concluded from the above facts that, under different working conditions, the failure rate of NC machine tools is different, and the traditional method does not consider the differences in the results of reliability evaluation caused by different working conditions.Therefore, the working conditions must be considered in evaluating the reliability level of NC machine tools.


## 4. Conclusions

The same NC machine tools under different working conditions have different reliability level. However, traditional reliability evaluation methods do not consider the impacts of working conditions on the reliability of NC machine tools. Considering this problem, the authors propose a new method for NC machine tools’ reliability evaluating. The Cox proportional hazards model is used to establish the mathematical relation model between the NC machine tools’ reliability and the corresponding working conditions. The likelihood ratio test is used to eliminate the working condition covariates which have no effects on the reliability of NC machine tools. In the parameter estimating process, a two-step estimation method is applied. That is, the coefficients of working condition covariates are firstly estimated by the partial likelihood estimation method, and then the maximum likelihood estimation method is used to estimate the parameters of the baseline failure rate function. Finally, a practical application case is presented. The new method is used for evaluating reliability of NC machine tools under different working conditions, which have 4 covariates (the cutting force, the number of tool changes, the cutting fluid, and the environment temperature). By the likelihood ratio test, the cutting fluid and the environment temperature (within the range of 19~22 degrees Centigrade) are eliminated, which have no effects on the reliability, and the mathematical relation between the cutting force, the number of tool changes, and the reliability is finally established. When the cutting force and the number of tool changes increase, the reliability of NC machine tools increases. For further comparison, the authors used a traditional method to evaluate the reliability of this batch of NC machine tools. The result of the case study indicates that, compared with the traditional method, the new method can establish the mathematical relation between the working conditions and the reliability, which makes up the defects of traditional methods and provides a more accurate basis for further reliability improvement design and reliability growth.



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*Source: 9842607-2016-05-04.xml*