Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.

A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

A fast reliable computational quality predictor is eagerly desired in practical image/video applications, such as serving for the quality monitoring of real-time coding and transcoding. In this paper, we propose a new perceptual image quality assessment (IQA) metric based on the human visual system (HVS). The proposed IQA model performs efficiently with convolution operations at multiscales, gradient magnitude, and color information similarity, and a perceptual-based pooling. Extensive experiments are conducted using four popular large-size image databases and two multiply distorted image databases, and results validate the superiority of our approach over modern IQA measures in efficiency and efficacy. Our metric is built on the theoretical support of the HVS with lately designed IQA methods as special cases.

A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures

Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems’ behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms’ robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.

Recent Advances in Key-Performance-Indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT

Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method.

Key-Performance-Indicator-Related Process Monitoring Based on Improved Kernel Partial Least Squares

Air quality is currently arousing drastically increasing attention from the governments and populace all over the world. In this paper, we propose a heuristic recurrent air quality predictor (RAQP) to infer air quality. The RAQP exploits some key meteorology- and pollution-related variables to infer air pollutant concentrations (APCs), e.g. the fine particulate matter (PM2.5). It is natural that the meteorological factors and APCs at the current time have strong influences on air quality the next adjacent moment, that is to say, there exist high correlations between them. With this consideration, applying simple machine learners to the current meteorology- and pollution-related factors can reliably predict the air quality indices at a time later. However, owing to the nonlinear and chaotic reasons, the above correlations decline with the time interval enlarged. In such cases, it fails to forecast the air quality after several hours by only using simple machine learners and the current measurements of meteorology- and pollution-related variables. To solve the problem, our RAQP method recurrently applies the 1-h prediction model, which learns the current records of meteorology- and pollution-related factors to predict the air quality 1 h later, to then estimate the air quality after several hours. Via extensive experiments, results confirm that the RAQP predictor is superior to the relevant state-of-the-art techniques and nonrecurrent methods when applied to air quality prediction.

Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares

The issue of quality-related fault detection has attracted much attention in recent years. Partial least squares (PLS) is considered as an efficient tool for predicting and monitoring. However, due to the fact that PLS performs an oblique projection to input space, it is not suitable for quality-related fault detection. On the other hand, PLS is a static method which cannot be used in dynamic systems. In this paper, a dynamic least squares approach is developed by using the structure of auto-regressive moving average exogenous (ARMAX) time-series model. Furthermore, augmented input matrix is decomposed into two orthogonal parts according to their correlations with output, such that quality-related fault detection can be utilized by designing appropriate statistics in two subspaces corresponding to the two parts. The proposed approached is simple and effective for systems with dynamic input and static output, which is a common case for most industrial processes. A numerical example and an industrial process simulator are applied to test the performance of the proposed approach.

A Quality-Related Fault Detection Approach Based on Dynamic Least Squares for Process Monitoring

In this paper, a nonlinear quality-related fault detection approach is proposed based on kernel least squares (KLS) model. The major novelty of the proposed method is that it utilizes KLS model to exploit the entire correlation between feature and output matrices. First, it uses a nonlinear projection function to map original process variables into feature space in which the correlation between feature and output matrices is realized by means of KLS. Then, the feature matrix is decomposed into two orthogonal parts by singular value decomposition and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with existing kernel partial least squares (KPLS) based approaches, the proposed new method has the following obvious advantages. 1) It extracts the full correlation information of feature matrix, while KPLS-based approaches only use the partial correlation of several selected latent variables; therefore, it is more stable than the existing ones. 2) It omits the iterative computation of KPLS model and the determination of the number of latent variables; therefore, it is more efficient in engineering implementation. 3) It only uses two statistics to determine the type of fault, while most of the KPLS-based approaches need four; therefore, it has a more simple diagnosis logic. For simulation verification, a widely used literature example and an industrial benchmark are utilized to evaluate the performance of the proposed method.

A Kernel Least Squares Based Approach for Nonlinear Quality-Related Fault Detection

Event triggered trajectory tracking control approach for fully actuated surface vessel

This article considers the issue of quality-related process monitoring. A novel kernel direct decomposition (KDD) algorithm is proposed and a KDD-based nonlinear quality-related fault detection approach is designed. The proposed KDD algorithm first maps original process variables into feature space to deal with the nonlinearities among these variables. Feature matrix is then directly decomposed into two orthogonal parts according to its full correlation with output matrix without building any regression model. Compared with conventional nonlinear methods, the KDD-based approach has the following advantages: 1) it is simpler in design as it omits the steps of constructing a regression model like kernel partial least squares (KPLS); 2) its performance is more stable because it extracts the full correlation information of feature matrix unlike KPLS-based methods which only use the partial correlation information of several selected latent variables; and 3) it has a simpler diagnosis logic since it only uses two statistics to determine the type of fault while most existing methods need four. Simulations on a literature example and a simulated industrial process are used to demonstrate the advantages of the new method.

Jianfang Jiao

Papers

A nonlinear quality-related fault detection approach based on modified kernel partial least squares

A Quality-Related Fault Detection Approach Based on Dynamic Least Squares for Process Monitoring

A Kernel Least Squares Based Approach for Nonlinear Quality-Related Fault Detection

Event triggered trajectory tracking control approach for fully actuated surface vessel

A Kernel Direct Decomposition-Based Monitoring Approach for Nonlinear Quality-Related Fault Detection