Special Issue "Entropy-Based Fault Diagnosis"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 31 May 2019
Prof. Dr. Spyros G. Tzafestas
Modern systems (chemical processes, power plants, robotic systems, manufacturing systems, automotive systems, etc.) are complex and large scale systems that are subject to faults, failures and malfunctions which degrade their operational performance, and may cause instability and safety problems, sometimes catastrophic. Thus, over the years, engineers have attempted to develop and apply proper techniques and fast algorithms for detecting, isolating, and diagnosing such faults and failures as quickly and accurately as possible. Typically, these techniques require information from several measurable or non-measurable system variables. In general, fault detection and diagnosis (FDD) techniques are distinguished in: (i) data techniques (PCA, spectrum techniques, pattern recognition techniques), (ii) model-based techniques (parity technique, parameter estimation, state estimation), and (iii) model-free techniques (expert systems, fuzzy logic methods, neural network methods, Hybrid methods, etc.). A recent development in the system FDD field is the use of information theoretic methods, and in particular entropy-based methods. The purpose of this Special Issue is exactly to include high quality theoretical and application papers that treat various FDD problems using the entropy-based approach or its combination with other approaches.
Specifically, the Special Issue will consider research and review papers using the following (non-inclusive) entropy-based FDD methods:
- Maximum entropy methods.
- Sample entropy methods.
- Approximate entropy methods.
- Single-scale and multi-scale entropy methods.
- Permutation entropy methods.
- Wavelet entropy methods.
- Fuzzy entropy methods.
- Singular entropy methods.
- Neural network entropy methods.
- Entropy-based complexity measures methods.
- Combinations of the above methods (hybrid methods).
Case study papers treating FDD problems of real-life practical systems, and presenting respective experimental/simulation results are mostly welcome.
Prof. Dr. Spyros G. Tzafestas
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Fault detection
- fault diagnosis
- fault isolation
- parameter estimation
- state estimation
- pattern recognition
- model-based fault diagnosis
- model-free fault diagnosis
- approximate entropy
- fuzzy entropy
- sample entropy
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Overview of Fault Detection and Diagnosis: The Entropy-Based Approach
Abstract: Fault detection and diagnosis (FDD) is a system theory branch of primary importance for the successful, efficient, and safe operation of technological systems. The aim of this paper is to provide an overview of FDD with focus on the entropy-based methodology. The paper starts with background material on information, Shannon entropy, approximate entropy (ApEn), and sample entropy (SampEn), including a brief review of the literature on ApEn and SampEn. Then, it provides an outline of the FDD concept, including a tour to FDD methods for dynamic systems. The general procedure for model-based FDD that involves the signal processing approach, to which the entropy-based methods belong, is then described. Next, an overview of the entropy-based FDD literature is given and the associated general FDD procedure is described. Finally, three entropy-based FDD examples, selected from the above reviewed literature, are outlined at some more detail to illustrate how the entropy-based feature extraction can be implemented and combined with available classifiers to complete the fault diagnosis.