Abstract:Optimal replacement pOlicy fOr a periOdically inspected system subject tO the cOmpeting sOft and sudden failures Optymalna pOlityka wymiany dO zastOsOwania w systemach pOddawanych przeglądOm OkresOwymnarażOnych na kOnkurujące uszkOdzenia parametryczne i nagłe demonstrated that the optimal replacement policy was a multi-level control-limit policy with monotonically increasing control limits. However, considering only the soft failure seems to be inadequate for the degrading systems that are also subject to the … Show more
“…To provide an illustration of the online assessment, the degradation history (t, X(t)) of a practical degrading system is supposed to be (0, 0), (1.5, 2), (3, 4), (4.5, 7), (6,9). The conditional PMF of RL can be calculated by (15) and conditional MRL can be calculated by (17).…”
Section: Fig 4 Conditional Rf For Several Monitoring Time Instants With Initial Degradation Statementioning
confidence: 99%
“…Note that one of the common features of the aforementioned works is that there is no dependent relationship between the soft and the hard failures. However, in most practical applications, the occurrence of the hard failure depends not only on the operational time, but it is also more likely to occur when the degradation level is higher [9,10], which indicates that the soft failure and the hard failure are dependent. Thus, the independence assumption of the soft and hard failure modes may cause underestimation or overestimation of the system health condition.…”
This paper proposes a health evaluation method for degrading systems subject to competing risks of dependent soft and hard failures. To characterize the time-varying degradation rate, the degradation process is determined by a non-stationary Gamma process and the soft failure is encountered when it exceeds a predefined critical level. For the hard failure, a Cox's proportional hazard model is applied to describe the hazard rate of the time to system failure. The dependent relationship is modeled by incorporating the degradation process as a time-varying covariate into the Cox's proportional hazard model. To facilitate the health characteristics evaluation, a discretization technique is applied both to the degradation process and the monitoring time. All health characteristics can be obtained in the explicit form using the transition probability matrix, which is computationally attractive for practical applications. Finally, a numerical analysis is carried out to show the effectiveness and the performance of the proposed health evaluation method.
“…To provide an illustration of the online assessment, the degradation history (t, X(t)) of a practical degrading system is supposed to be (0, 0), (1.5, 2), (3, 4), (4.5, 7), (6,9). The conditional PMF of RL can be calculated by (15) and conditional MRL can be calculated by (17).…”
Section: Fig 4 Conditional Rf For Several Monitoring Time Instants With Initial Degradation Statementioning
confidence: 99%
“…Note that one of the common features of the aforementioned works is that there is no dependent relationship between the soft and the hard failures. However, in most practical applications, the occurrence of the hard failure depends not only on the operational time, but it is also more likely to occur when the degradation level is higher [9,10], which indicates that the soft failure and the hard failure are dependent. Thus, the independence assumption of the soft and hard failure modes may cause underestimation or overestimation of the system health condition.…”
This paper proposes a health evaluation method for degrading systems subject to competing risks of dependent soft and hard failures. To characterize the time-varying degradation rate, the degradation process is determined by a non-stationary Gamma process and the soft failure is encountered when it exceeds a predefined critical level. For the hard failure, a Cox's proportional hazard model is applied to describe the hazard rate of the time to system failure. The dependent relationship is modeled by incorporating the degradation process as a time-varying covariate into the Cox's proportional hazard model. To facilitate the health characteristics evaluation, a discretization technique is applied both to the degradation process and the monitoring time. All health characteristics can be obtained in the explicit form using the transition probability matrix, which is computationally attractive for practical applications. Finally, a numerical analysis is carried out to show the effectiveness and the performance of the proposed health evaluation method.
“…For the price scenario 04150, the largest number of continuous price periods is the average price, i.e. max[T cL , T cM , T cH ]=max [3,7,3]= T cM . And the sum of the periods for different price levels are [T sL , T sM , T sH ]= [9,18,9].…”
Section: Influence Of Electricity Price Scenarios 4)mentioning
confidence: 99%
“…For the threshold based on a condition monitoring index [7,24], the condition monitoring index can be obtained from monitoring items, such as wear, temperature, pressure, etc. For example in [24] the PM threshold was based on the wear measurement, while a kind of control-limit based on laser's operating current was studied in [7]. Another type of threshold is set on the integrated reliability index, and it is derived from both event data and condition monitoring data [9,14].…”
An electricity price-dependent control-limit policy for conditionbAsed mAintenAnce optimizAtion for power generAting unit zAstosowAnie strAtegii uzAleżniAjącej termin przeglądu od ceny prądu elektrycznego do optymAlizAcji utrzymAniA ruchu AgregAtu prądotwórczego z uwzględnieniem jego stAnu technicznego QIAN X, WU Y. An electricity price-dependent control-limit policy for condition-based maintenance optimization for power generating unit. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2016; 18 (2): 245-253, http://dx
“…Wu et al [32] investigated the reliability and quality problems when the competing risks data are progressive type-I interval censored with binomial removals. Tang et al [28] studied a replacement problem for a continuously system subject to the competing risk of soft and sudden failures.…”
Reliability analysis of the pRoducts subject to competing failuRe pRocesses with unbalanced data opaRta na nieZbilansowanych danych analiZa nieZawodnoŚci pRoduKtÓw podlegajĄcych pRocesom powstawania usZKodZeŃ KonKuRujĄcych LI J, ZHANG Y, WANG Z, FU H, XIAO F. Reliability analysis of the products subject to competing failure processes with unblanced data. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2016; 18 (1): 98-109, http://dx
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