Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminium electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature

A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods

In prognostics and health management, the prediction capability of a prognostic method refers to its ability to provide trustable predictions of the remaining useful life, with the quality characteristics required by the related maintenance decision making. The prediction capability heavily influences the decision makers' attitude toward taking the risk of using the predicted remaining useful life to inform the maintenance decisions. In this article, a four-layer, top-down, hierarchical decision-making framework is proposed to assess the prediction capability of prognostic methods. In the framework, prediction capability is broken down into two criteria (Layer 2), six sub-criteria (Layer 3) and 19 basic sub-criteria (Layer 4). Based on the hierarchical framework, a bottom-up, quantitative approach is developed for the assessment of the prediction capability, using the information and data collected at the Layer-4 basic sub-criteria level. Analytical hierarchical process is applied for the evaluation and aggregation of the sub-criteria and support vector machine is applied to develop a classification-based approach for prediction capability assessment. The framework and quantitative approach are applied on a simulated case study to assess the prediction capabilities of three prognostic methods of the literature: fuzzy similarity, feed-forward neural network and hidden semi-Markov model. The results show the feasibility of the practical application of the framework and its quantitative assessment approach, and that the assessed prediction capability can be used to support the selection of the suitable prognostic method for a given application

A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity-based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity-based models

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber–physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models

Predictive maintenance for industry 5.0:behavioural inquiries from a work system perspective

Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations.</p