Behavior prediction of washing system in a paper industry using GA and fuzzy lambda–tau technique

Availability analysis has been an important issue in the design field of any Industrial system as the system structure has become more complicated. Also, the system availability is affected by many factors such as design, manufacturing, installation, etc., and so it may be extremely difficult to model, analyze and predict the failure behavior of the system. The purpose of this paper is to develop a new approach for computing various performance measures, namely reliability, availability, MTBF (mean time between failures), ENOF (expected number of failures), failure rate and repair time, for any industrial system. In the proposed approach, the failure rates and repair times of all constituent components are obtained using genetic algorithms and then various performance measures are computed using fuzzy lambda–tau methodology. Washing system, the major part of paper industry is the subject of study. The interactions among the working components are modeled using Petri nets. Failure and repair rates are represented using triangular fuzzy numbers as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into system model. Based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior.     

A study of repairable parts inventory system operating under performance-based contract

Performance-Based Logistics (PBL) is becoming a dominant logistics support strategy, especially in the defense industry. PBL contracts are designed to serve the customer’s key performance measures, while the traditional contracts for after-sales services, such as Fixed-price (FP) and Cost-plus (C+), only provide insurance or incentive. In this research, we develop an inventory model for a repairable parts system operating under a PBL contract. We model the closed-loop inventory system as an M/M/m queue in which component failures are Poisson distributed and the repair times at the service facility are exponential. Our model provides the supplier and the customer increased flexibility in achieving target availability. Analysis of key parameters suggests that to improve the availability of the system with repairable spare parts, the supplier should work to improve the components reliability and efficiency of repair facility, rather than the base stock level, which has minimal impact on system availability.     

Optimum Redundancy with Equipment that Operates and Idles

Despite their widespread exclusion from the literature on reliability and replacement, many items of equipment are subject to failures to idle as well as failures to operate. For such equipment a new optimisation problem arises at the stage of systems design in terms of the optimal use of redundancy to maximise expected systems life. The models hitherto used as the basis for the solution of this optimization problem ignore the dependence that should exist for the solutions upon the operating and idling requirements to which the system is to be exposed. In this paper a simple model for equipment subject to such opposite failure modes is constructed which takes explicit account of the proportion of time the equipment is in use. The implications of this new model for the selection of an optimum redundancy configuration are illustrated for the case where four identical items of equipment are available.     

A note on replacement policy for a system subject to non-homogeneous pure birth shocks

A system is subject to shocks that arrive according to a non-homogeneous pure birth process. As shocks occur, the system has two types of failures. Type-I failure (minor failure) is removed by a general repair, whereas type-II failure (catastrophic failure) is removed by an unplanned replacement. The occurrence of the failure type is based on some random mechanism which depends on the number of shocks occurred since the last replacement. Under an age replacement policy, a planned (or scheduled) replacement happens whenever an operating system reaches age T. The aim of this note is to derive the expected cost functions and characterize the structure of the optimal replacement policy for such a general setting. We show that many previous models are special cases of our general model. A numerical example is presented to show the application of the algorithm and several useful insights.     

Impulsive Markov jump linear systems: Stability analysis and H2 control

In this work, we present an impulsive Markov jump linear system model. We show how the present model generalises previous works from the literature, and we devise necessary and sufficient conditions for stability and performance, together with mode-dependent state-feedback control design conditions for such systems. An applied example shows how the developed theory can be used to control strategies under actuator and sensor failures.     

Model based fuzzy control

Fuzzy controllers can be made to adapt to changing process and environment dynamics. This paper presents one methodology to adapt the initial knowledge base to changing operating conditions. The membership functions associated with the process controller output are adjusted in response to future or past performance of the feedback control system. A linearized process model is identified on line and is used to predict the future performance to the controller. This performance index is used to adapt the controller as long as the identified process model is statistically reliable. If the process model is considered unreliable, then an index related to the past performance of the controller is used to make the adjustments.     

基于非齐次泊松过程的拖拉机故障规律性研究

进行了拖拉机田间作业故障跟踪试验 ,得到了拖拉机现场使用的故障数据 ,用非齐次泊松过程建立了拖拉机故障过程的数学模型 ,并用神经网络方法给出了模型的参数估计 ,讨论了拖拉机故障过程的改善与劣化与故障强度函数的关系 ,得到了拖拉机使用初期的故障强度曲线 ,给出了平均故障间隔时间的估计值 .     

A reliability semi‐Markov model involving geometric processes

We consider a semi‐Markov process that models the repair and maintenance of a repairable system in steady state. The operating and repair times are independent random variables with general distributions. Failures can be caused by an external source or by an internal source. Some failures are repairable and others are not. After a repairable failure, the system is not as good as new and our model reflects that. At a non‐repairable failure, the system is replaced by a new one. We assume that external failures occur according to a Poisson process. Moreover, there is an upper limit N of repairs, it is replaced by a new system at the next failure, regardless of its type. Operational and repair times are affected by multiplicative rates, so they follow geometric processes. For this system, the stationary distribution and performance measures as well as the availability and the rate of occurrence of different types of failures in stationary state are calculated. Copyright © 2002 John Wiley & Sons, Ltd.     

Reliability of a system under two types of failures using a Markovian arrival process

We consider a system subject to external and internal failures. The operational time has a phase-type distribution (PH-distribution). Failures arrive following a Markovian arrival process (MAP). Some failures require the replacement of the system, and others a minimal repair. This model extends previous papers with arrivals governed by PH-renewal processes.     

A model for a Mixed Continuous-Periodic Review One-Warehouse,N-Retailer inventory system

This paper analyzes a Mixed Continuous-Periodic Review One-Warehouse, N-Retailer inventory system for a single, consumable item. In this system, the warehouse holds stock and the retailers experience independent, stationary field demand. Each retailer follows a continuous review (Q, r) policy. However, the warehouse reviews each retailer according to a review interval T. We discuss the motivation for this warehouse policy and show how its impact on the retailers can be modeled with an (nQ, r, T) policy at the retailers and the warehouse. Then we present an approximate analytical model to predict the performance of such a system under different operating conditions. An interesting aspect of the model is the estimation of warehouse demand variance via a closed form expression derived using renewal theory. We compare the approximate model with a simulation model to test its accuracy. The comparison indicates that the approximate model predicts quite accurately in high fill rate ranges (retailer fill rates of 90% and more). Finally, we propose several extensions to this research.     

基于耦合映像格子的城市交通系统相继故障研究

在对一个实际的城市交通系统进行复杂网络描述的基础上,构造了该城市交通系统的耦合映像格子模型,利用该模型研究了城市交通系统的相继故障问题,应用计算机仿真手段研究了干扰强度和网络相继故障的关系,网络相继故障在攻击条件下对于节点度数的敏感性以及不同攻击策略下网络相继故障的传播问题,对实际城市交通系统的规划、设计、建设和管理具有现实意义.     

Estimation of the number of failures in the Weibull model using the ordinary differential equation

In estimating the number of failures using right truncated grouped data, we often encounter cases that the estimate is smaller than the true one when we use the likelihood principle to conditional probability. In infectious disease spread predictions, the SIR model described by simultaneous ordinary differential equations is commonly used, and it can predict reasonably well the number of infected patients even when the size of observed data is small. We have investigated whether the ordinary differential equation model can estimate the number of failures more accurately than does the likelihood principle under the condition of right truncated grouped data. The positive results are obtained in the Weibull model, similarly to the cases of the SARS, A(H1N1), and FMD.     

Optimal age-replacement time with minimal repair based on cumulative repair-cost limit for a system subject to shocks

An operating system is subject to random shocks that arrive according to a non-homogeneous Poisson process and cause the system failed. System failures experience to be divided into two categories: a type-I failure (minor), rectified by a minimal repair; or a type-II failure (catastrophic) that calls for a replacement. An age-replacement model is studied by considering both a cumulative repair-cost limit and a system’s entire repair-cost history. Under such a policy, the system is replaced at age T, or at the k-th type-I failure at which the accumulated repair cost exceeds the pre-determined limit, or at any type-II failure, whichever occurs first. The object of this article is to study analytically the minimum-cost replacement policy for showing its existence, uniqueness, and the structural properties. The proposed model provides a general framework for analyzing the maintenance policies, and presents several numerical examples for illustration purposes.     

A model for real-time failure prognosis based on hidden Markov model and belief rule base

As one of most important aspects of condition-based maintenance (CBM), failure prognosis has attracted an increasing attention with the growing demand for higher operational efficiency and safety in industrial systems. Currently there are no effective methods which can predict a hidden failure of a system real-time when there exist influences from the changes of environmental factors and there is no such an accurate mathematical model for the system prognosis due to its intrinsic complexity and operating in potentially uncertain environment. Therefore, this paper focuses on developing a new hidden Markov model (HMM) based method which can deal with the problem. Although an accurate model between environmental factors and a failure process is difficult to obtain, some expert knowledge can be collected and represented by a belief rule base (BRB) which is an expert system in fact. As such, combining the HMM with the BRB, a new prognosis model is proposed to predict the hidden failure real-time even when there are influences from the changes of environmental factors. In the proposed model, the HMM is used to capture the relationships between the hidden failure and monitored observations of a system. The BRB is used to model the relationships between the environmental factors and the transition probabilities among the hidden states of the system including the hidden failure, which is the main contribution of this paper. Moreover, a recursive algorithm for online updating the prognosis model is developed. An experimental case study is examined to demonstrate the implementation and potential applications of the proposed real-time failure prognosis method.     

Bayesian inference for the power law process

The power law process has been used to model reliability growth, software reliability and the failure times of repairable systems. This article reviews and further develops Bayesian inference for such a process. The Bayesian approach provides a unified methodology for dealing with both time and failure truncated data. As well as looking at the posterior densities of the parameters of the power law process, inference for the expected number of failures and the probability of no failures in some given time interval is discussed. Aspects of the prediction problem are examined. The results are illustrated with two data examples.     

Long-term average control of a continuous,monotone process

We analyze optimal control problems for systems subject to random deterioration and failure. The system is replaced at failure and our objective is to optimize the utilization of the system between failures. The problems are new in that the payoff depends on the running maximum of a diffusion. This provides an intuitively appealing model for naturally monotone phenomena such as wear. The long-term average control problem is reduced to a family of simpler, single-cycle problems, a formula for the invariant measure for the (controlled) process is determined and a computational scheme based on the decomposition and formula is given.     

Power processes and their application to reliability

We introduce the stochastic process we call a power process and show that under certain conditions it is stochastically monotone. We use it to model a deteriorative system where operating times and repair times are general discrete random variables, and illustrate our results with numerical examples.     

Simulation of fluid catalytic cracking operation

Since its introduction in 1942 the fluid catalytic cracking (FCC) has been the most important and widely used process for the production of gasoline from heavy distillates. In most refineries the capacity of the FCC unit is second only to that of the crude distillation unit. Often an FCC unit is referred to as the heart of a modern refinery oriented toward maximum production of gasoline.The basic step in the FCC process is the recirculation of the catalyst through the reactor, stripping and regenerator. In the reactor system the hydrocarbon feed is heated and cracked. Coke (or carbon) may be produced and may deposit on the catalyst reducing its activity and selectivity. When the catalyst is circulated to the regenerator carbon is burned off causing the heating of the catalyst before its return to the reactor part. The products from the reactor are separated in a main fractionator into gas and liquid streams normally including a recycle feed to the reactor.The operation of an FCC unit requires the manipulation of a large number of controlled variables affecting its performance. Major process variables such as reactor temperature, catalyst circulation rate, catalyst inventory and recycle feed rate can be varied to influence the product yields and to accomodate widely different feedstocks. Unpredictable variation can occur in feed stock, catalyst quality and equipment performance. Most normal variation can be accomodated by a small change in operating conditions.For a new plant, comparison of actual versus predicted performance provides a valuable check on the validity of the design correlations and a guide for future laboratory and engineering research.The objectives of the present work are to simplify the complicated FCC process variables and to develop a computer model to simulate the operation of an FCC at different conditions. This includes the prediction of the effects of the operating variables on the reactor product yields. These products include fuel gas, C₃, C₄ gasoline, light gas oil and coke. The model provides a good base for troubleshooting and debottlenecking and may be useful in optimal control of the FCC.     

Detection of incipient gear failures using statistical techniques

Gears are important components in most power transmission mechanisms.Failures of gears can cause heavy losses in industry. Conditionmonitoring and fault diagnosis of gears is therefore importantto improve safety and reliability of gearbox operations andreduce losses caused by gear failures. This research proposesa new diagnostic approach based on the statistical analysisof data. It investigates the use of Principal Components Analysis(PCA) to detect growing local faults in a two-stage industrialhelical gearbox. In this research, the vibration signal is usedto monitor fault conditions and a broken tooth is simulatedas a local fault. Since the early detection of faults is a challenge,small fault conditions were tested first as well as severe faultconditions. In order to examine the ability of the PCA to detectfault conditions, first the PCA-based model was created fornormal operating conditions. Any unexpected event such as afault condition causes a significant deviation from the PCAmodel, which is obtained from the normal condition data of thegearbox. The Square Prediction Error (SPE) was calculated todetect the fault conditions. When the vibration signal fromthe gearbox is representative of normal operation, the valueof the SPE shows very little fluctuation and remains under acertain threshold value. However, in the presence of the faultthe SPE fluctuates considerably beyond the threshold value.It is shown that the PCA-based statistical approach cannot onlybe used to detect severe fault conditions, but that it alsoreveals small growing fault conditions at very early stage.The technique also provides information about the state of thefault such as the location of the fault as well as its severity. Received 5 March 2001. Revised 12 December 2001. Accepted 17 January 2002.     

Modelo de un proceso de cristalización continua de un sorbete por medio de la metodología de momentos

Freezing is an important step in the manufacturing process of ice-cream and sorbet, since the operating conditions have a strong influence on the micro-structure, and consequently on the sensorial attributes of the final product. This steep of freezing is carried out by a scraped surface heat exchanger (SSHE) where the product quality is conditioned by process conditions as the evaporation temperature of a refrigerant fluid, the mix flow rate, the dasher speed and the cylinder pressure due to the air introduction. In order to study the relevance of a control system based on the influence of process variables on product quality, this paper presents a model for a continuous crystallization of a sorbet using the method of moments, which is validated by experimental data.The model created by this methodology has been able to represent the influence of the process conditions during the crystallization of the sorbet on the final product characteristics such as crystal size and the draw temperature in the outlet of the SSHE in absence of air. The model based in moments is studied as a reduced model of the population balance equation and includes the phenomena of heterogeneous nucleation and growth. This model developed represents minimal computational requirements and is highly adapted for optimization and/or process control tasks.