Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity , each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.

Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction

The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

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A Review on Fault Detection and Process Diagnostics in Industrial Processes

This paper proposes an improvement on observer design of discrete-time fuzzy systems based on an alterable weights method. Different from the recent result, a more effective ranking-based switching mechanism is developed by introducing a bank of alterable weights for the sake of making use of the size difference information of the normalized fuzzy weighting functions more freely than before. Therefore, a positive result can be provided in this paper, that is, less conservative conditions of designing feasible fuzzy observers can be obtained than those existing results, while the computational cost of designing feasible fuzzy observers is even less than the up-to-date one. Finally, two numerical examples are given to show the progressiveness of the proposed method.

Observer Design of Discrete-Time Fuzzy Systems Based on an Alterable Weights Method

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

A review on fault detection and diagnosis techniques: basics and beyond

Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.

Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview

As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.

Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Data-driven based fault prognosis for industrial systems: a concise overview

The conventional distributed modeling strategy generally includes all the process variables in large-scale process monitoring, thus submerging the local fault information. Meanwhile, fault diagnosis issues in the aforementioned process are also worth studying. To make up the deficiencies of the general distributed method, this brief presents an advantageously distributed fault monitoring and diagnosis scheme for large-scale dynamic processes. First, the optimal variable subblock is established by the minimal redundancy maximal relevance (mRMR) algorithm for each measured variable individually. Second, the corresponding monitoring model is set up in every subblock, respectively. Finally, all the distributed multiblocks’ results are combined to form an integrated decision through the Bayesian inference. The proposed scheme considers not only the interpretation of the correlation of variables but also the redundancy between them in the block division, which better characterizes the dynamic relationships among variables, thereby facilitating the monitoring performance significantly. After that, a new contribution plot method based on subblock analysis is applied for fault isolation. Both a real-world industrial process and the Tennessee Eastman benchmark process validate that the novel monitoring strategy is more efficient than the existing state-of-the-art approaches.

Distributed Dynamic Process Monitoring Based on Minimal Redundancy Maximal Relevance Variable Selection and Bayesian Inference

The diesel engine is the core power equipment of ships, so keeping it in good working condition is vital for maintaining the overall efficiency of sea transportation. Generally speaking, the principal component analysis (PCA) based fault detection methods are highly depend on the unlabeled information of training data, that is means, the information contained in labeled data is largely ignored. However, the diesel engine working process is always accompanied with small amount of faulty samples, which may seriously affect the performance of the PCA model when the faulty samples are included for modeling. Besides, even though there are enough faulty samples for modelling, labeling these samples is also time-consuming and costly. In this paper, a semi-supervised PCA (SSPCA) is proposed and applied to diesel engine fault diagnosis instead of unsupervised learning, which incorporates both the labeled and unlabeled samples and gains enhanced fault diagnosis performance regarding marine diesel engine. The methodology presented is evaluated in the real-world diesel engine working process, the experimental results indicate good robustness to false alarms and certain practical significance towards the fault diagnosis of engineering object.

Kai Zhong

Papers

Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview

Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis

Data-driven based fault prognosis for industrial systems: a concise overview

Distributed Dynamic Process Monitoring Based on Minimal Redundancy Maximal Relevance Variable Selection and Bayesian Inference

Fault Detection for Marine Diesel Engine Using Semi-supervised Principal Component Analysis