Background

Forecasting the useful life of a system and its components fascinates researchers in various areas and has an important meaning especially in such fields like structural health monitoring (SHM), reliability of electronic equipment, both hardware and software components of computer systems, as well as process diagnosis and predictive-proactive maintenance in industrial systems. The objective of online reliability prediction is to estimate a time of upcoming nonoperational state at the current operational state of a system through real-time monitoring operational parameters and/or performances. The online reliability prediction provides information to support the diagnostics and decision-making process by advance alarming about failures and estimation of probability that a system is capable of operating satisfactorily in a given period of time. Forecasting the operating time of system equipment leads to reduce cost of unscheduled maintenance through timely repair actions, planning preventive and corrective operations, resources managing, and enhance availability by increasing maintenance effectiveness and decreasing downtime.

The automatic or semi-automatic process of discovering the predictive pattern from data set involves the methods or their hybrid approaches such as statistical techniques (regression, ARMA models, Bayesian probability distribution estimation, etc.), grey system theory, and soft computing methods. The artificial intelligence is frequently addressed to the predictive problem by utilizing the learning capability of artificial neural network (ANN), possibility of nonlinear mapping using fuzzy rules-based system (FRBS), or recognizing and optimizing data-derived pattern by using evolutionary algorithms.

This special section aims to provide an open, multidisciplinary forum for recent advances in soft computing techniques for reliability prediction technologies such as scientific works and applications utilizing the gray system theory, heuristic techniques, fuzzy logic, ANN, wavelet network, fuzzy wavelet network, and genetic algorithm (GA) for failure prediction.

We are soliciting original contributions that have not been published and are not currently under consideration by any other journals. Both theoretical studies and state-of-the-art practical applications are welcome. All submitted papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this special section.

Topics of Interest

Topics of interest include, but are not limited to, the following:

Submission

We welcome high quality submissions that are original work, not published, and not currently submitted elsewhere. We also encourage extensions to conference papers, unless prohibited by copyright, if there is a significant difference in the technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the difference between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors’ own papers and dissertations, should be less than 30%. Each submission must conform to the IJPE template which can be downloaded on the journal’s official website. Please submit your paper to the IJPE submission page.

Important Dates

Guest Editors