Prediction of system reliability using failure types of components based on Weibull lifetime distribution

Nowadays, products are complicated and standards of reliability are higher than ever before. In contrast, the time available for developing products has gradually decreased. In this context, assessment of the system reliability of products is an active field of study. In this paper, an experimental design was used to investigate the effect of the failure type of components on the whole system’s failure type. For this, a general procedure for estimating system reliability based on the Weibull distribution was adopted.

Flexible manufacturing cells (FMC) are used to process a variety of products in the system and provide higher productivity. The components of the system such as robots and machines are more likely to fail when compared with the traditional manufacturing system due to their higher operating rate. Literature studied reveals that the failures of the machines have a high impact on the production rate of the system. In this work, two types of failures are considered i.e. wear-out failure and random failure. Wear-out failure can be eliminated by applying some maintenance plans but random failures are difficult to eliminate. So simulation experiments along with maintenance plans are studied to eliminate such failures. We also discussed two types of failure rate distributions i.e. exponential, and Weibull distribution and their effect on the throughput of the FMC. In this analysis, ARENA simulation model is made and simulation experiments were performed to analyze the throughput of FMC that consists of 2 machines in parallel and a robot for material handling. We assumed that failure is occurring in machines only and the robot is reliable throughout the simulation.

An approach to system reliability under wearout and overload failure is outlined, where conventional models for these mechanisms are used to define wear-dependent failure rates in terms of response “peak” statistics. System-failure rates are seen to be bounded above, making predictions based on the Weibull distribution too conservative at the “tail”, while certain normal-derived predictions are too optimistic. However, Weibull distributions are found quite useful in approximating the various response peak statistics for certain impulsive-noise environments encountered in random vibrations and elsewhere. The pitfalls of misusing “normalized” curves of reliability are cited and the need for future investigations is expressed.

This paper discusses the problem of system reliability prediction by accelerated testing results on its components. This problem appears when the testing of a system as a whole either has high cost or may be impossible in a short period of time, especially at the beginning of development. A multi-variate Weibull model is proposed to utilize testing results of components in predicting system reliability with reduced test length and minimized cost. Prediction of system reliability in a given situation means that we must know how to calculate not only the point estimation of reliability index, but also the lower confidence bound (LCB) with given confidence probability q (q=0.8-0.95). In this case, the authors' goal is to give the algorithm for calculating not by approximation for LCB (based as a rule on normal distribution of point reliability estimation), but exact LCB corresponding to a given confidence probability.

This paper focuses on analysis and prediction of system reliability by using methods for achieving data-driven, customer focused reliability and availability assessment. The Markov models can be used to create system level architecture-based models that allow assessment of reliability and available parameter estimation on a system level. Analysis of finer granularity provides more insights on the root cause of problems from the customer view and future work also addressed how this can be made practical and useful.

Today, numerous reliability prediction methods exist for electronic systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure electronic components/system's reliability. Based on such a method's merits and limitations, we recommend using the hybrid model to predict an electronic system's reliability prediction in the distinct development phase of the product life cycle to get higher accuracy.

This paper studies the reliability measures of a system consisting of two subsystems in a series configuration for different types of failure and two types of repair. The subsystem-1 has four identical units in a parallel configuration operating under 3-out-of-4: G policy and this has connected to subsystem-2 which has three identical units arranged in a parallel configuration and working under 2-out- of-3: F, scheme. The units of subsystem-1 are controlled by a controller for preventing failure effect and safety purposes. It is assumed that units of each subsystem have different types of failure and repair rates. The unsuitability of the environmental conditions such as overheating as a well-known natural cause of failure of any system and also weather conditions like heavy rain, thunderstorm, and catastrophic shakeups, etc. have treated as a complete failure of the system. This study considers the environmental causes of failure in the proposed repairable system as complete failure by which the system stops functioning. Human failure in the system is trickled as complete failure and repair employing copula (Gumbel- Hougaard family copula distribution) like another complete failed states of subsystems. To analyze the proposed system, the supplementary variable technique are used and some measures of system reliability like availability, reliability; MTTF and incurred profit function for different values of parameters are derived. Some particular cases for different values of failure rates that have explicit are also studied.

Manufacturing systems are increasingly becoming automated and complex in nature. Highly reliable and flexible manufacturing systems (FMSs) are the necessity of manufacturing industries to fulfill the increasing customized demands. Worldwide, FMSs are used in industries to attain high productivity in production environments with rapidly and continuously changing manufactured goods structures and demands. Reliability prediction plays a very significant role in system design in the manufacturing industry, and two crucial issues in the prediction of system reliability are failures of equipment and system configuration. This novel work presents a stochastic model to analyze the performance of an FMS through its reliability characteristics, in the concern of its equipment. To improve the reliability of FMS, determine the sensitivity of the reliability measures of FMS. FMS consists of many components such as machine tools like CNC, automatic handling and material storage, controller and robot for serving load. The designed system is studied by using the Markov process, supplementary variable technique, Laplace transformation, coverage factor and Gumbel–Hougaard family copula to obtain various reliability measures. For some realistic approach, particular cases and graphical illustrations are also obtained.

This study was conducted to examine disproportionality, times to onset, incidence rates and outcomes of lenvatinib-associated cardiac adverse events (AEs) using the Japanese Adverse Drug Event Report database. We analysed data for the period between April 2004 and May 2023. Data on cardiac AEs were extracted and the relative disproportionality of AEs was estimated using reporting odds ratios (RORs). Furthermore, Weibull distribution parameters were calculated. Of the 2 230 863 reports analysed, we identified 7684 reports of AEs associated with lenvatinib, including 317 cardiac AEs. Signals were detected for eight cardiac AEs: hypertension, cardiac failure, myocarditis, myocardial infarction, immune-mediated myocarditis, cardiomyopathy, angina unstable and cardiotoxicity. Among these, fatal outcomes were observed for cardiac failure, myocarditis and myocardial infarction. Histograms of median times to onset for the eight detected cardiac AE signals showed that AEs occurred at a median of 3.5-134.5days after lenvatinib administration. The Weibull distributions showed that cardiac failure occurred early after administration (early failure type), myocarditis occurred in a dose-dependent manner (wearout failure type), and myocardial infarction occurred constantly throughout the exposure period (random failure type). We focused on cardiac AEs associated with lenvatinib as post-marketing AEs. Serious outcomes can arise after lenvatinib administration. Patients should be monitored for signs of onset of these AEs not only at the start of administration, but also over an extended period.

The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.

This study is a survey to evaluate the flexible pavement conditions to determine and specify the types of the failures in the pavement for the selected highway. It is very significant to evaluate and identify the causes of the flexible pavement failures and select the proper and best treatment and maintenance type. The study had two major and critical goals which covered by considering the following three tasks; the first was the visual evaluation and inspection of existing flexible pavement conditions including the failures, the second to determine and find out the actual causes of these failures in the pavement, and the third is to select the most and effective treatments and maintenance types. As a case study, Khanaqin-Kalar 2-way 2-lane rural highway was selected for evaluation and inspection purposes. The field evaluation works were achieved on the existing flexible pavement conditions of the selected rural highway. The results were most of the damages and failures in the pavement are serious and extreme surface deformation, cracks, disintegration, and surface defects. These damages and failures are caused by fatigue and other types of failures resulted from the movement of heavy vehicles and trucks, poor drainage design, unsuitable pavement layers thickness design, and improper pavement mix design and selected materials.

In order to ensure high reliability of complex engineered systems against deterioration or natural and man-made hazards, it is essential to have an efficient and accurate method for estimating the probability of system failure regardless of different system configurations (series, parallel, and mixed systems). Since system reliability prediction is of great importance in civil, aerospace, mechanical, and electrical engineering fields, its technical development will have an immediate and major impact on engineered system designs. To this end, this chapter presents a comprehensive review of advanced numerical methods for system reliability analysis under uncertainty. Offering excellent in-depth knowledge for readers, the chapter provides insights on the application of system reliability analysis methods to engineered systems and gives guidance on how we can predict system reliability for series, parallel, and mixed systems. Written for the professionals and researchers, the chapter is designed to awaken readers to the need and usefulness of advanced numerical methods for system reliability analysis.

In the competing risks problem, an important role is played by the cumulative incidence function (CIF), whose value at time t is the probability of failure by time t from a particular type of failure in the presence of other risks. In some cases there are reasons to believe that the CIFs due to various types of failure are linearly ordered. El Barmi et al. (3) studied the estimation and inference procedures under this ordering when there are only two causes of failure. In this paper we extend the results to the case of k CIFs, where k ≥ 3. Although the analyses are more challenging, we show that most of the results in the 2-sample case carry over to this k-sample case.

Simulation-based system reliability prediction may require significant computations, particularly when the expected value of the system failure probability is relatively low. A methodology is presented for variance reduction of sampling-based series system reliability predictions based on optimal allocation of Monte Carlo samples to the individual failure modes. An algorithm is presented for adaptively allocating samples to member failure modes based on initial estimates of the member failure probabilities pi. The methodology is demonstrated for a simple series system and a gas-turbine engine disk modeled using a zone-based series system approach. For the example considered, it is shown that the computational accuracy of the method does not appear to depend on the initial pi estimate. However, the computational efficiency is highly dependent on the initial pi estimate. The results can be applied to improve the efficiency of sampling-based series system reliability predictions.

Analysis of Arterial Blood Gas (ABG) is an important investigation to measure oxygenation and blood acid levels. It is crucial in measuring the clinical status and contributes to an efficient and effective healthcare plan. Generally, ABG is applied in the emergency care units (ECU) and intensive care units (ICU). Most of the time, the doctors and nurses have difficulties identifying the type of respiratory failure with the help of ABG test results. So, during this research with the adaption of certain supervised machine learning approaches, namely Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Catboost, Random Forest, Naïve Bayes, Support Vector Machine (SVM), LightGBM, K-Nearest Neighbors (KNN), Neural Network (NN) and Decision Tree and have been incorporated with the intension of identifying the type of the respiratory failure with the highest accurate technique. To fulfil this purpose, 700 patient test results have been obtained from a public hospital in Sri Lanka. From the results discovered, XGBoost outperformed against all other techniques in identifying the type of respiratory failure with the highest accuracy of 98.65% and the lowest error rate of 1.35%. To ensure whether the XGBoost outperformed against the different percentages of training and testing data, K-fold cross-validation with five folds also has been performed with the dataset. The cross-validation produces results with an accuracy of 98.45% and the lowest error rate of 1.55%. In conclusion, XGBoost has been utilised in developing the prediction model. This would be a promising start for a future research scholar to adopt the hybrid techniques and the deep learning techniques to identify the causes of respiratory failure and the prediction of the type of respiratory failure.