Kaixiang Peng

A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill

In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process

Increasing global competition is setting even higher demands for the safety, quality and operating efficiency of industrial processes. The traditional projection to latent structures (PLS) based methods for quality-relevant fault detection has appeared in several industrial applications, while total PLS that performs more completely has been used as a better tool for monitoring associated with the product quality. However, the running/operating states for the process variables are often non-stationary, time-varying. Thus, the static PLS or TPLS for these processes will reduce the efficiency of the monitoring, unreliable monitoring results will affect the engineers? decision-making. Under this background, an adaptive modification on total PLS model named as recursive TPLS will be proposed to adapt the monitoring model on line. The new recursive version is achieved via a far more computation-efficient manner and the operating cost is significantly lowered. The simulation on TE process illustrates the effectiveness of the new adaptive fault monitoring approach based on RTPLS.

A Quality-Based Nonlinear Fault Diagnosis Framework Focusing on Industrial Multimode Batch Processes

This paper proposes a framework for quality-based fault detection and diagnosis for nonlinear batch processes with multimode operating environment. The framework seeks to address 1) the mode partition problem using a kernel fuzzy C-clustering method, and the optimal cluster number will be guaranteed by a between-within proportion index; 2) the diagnosis problem using a contribution rate method based on an improved kernel partial least squares (PLS) model, by which better detection and diagnosis performances are provided; and 3) the classification of online measurements using a hybrid kernel PLS regression and the Bayes inference theory, where the new coming measurement can be correctly assigned to its constituent mode. The whole framework is developed for batch processes, and applied to the hot strip mill rolling process. It is shown using the real industrial data that for faults affecting the thickness and flatness of the strip steel in this process, the detection and diagnosis abilities of the present methods are better compared with the existing methods.

Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements

Abstract Modern hot strip mill process (HSMP) is increasingly characterized by producing small-batch, multi-specification and high-value-added products. Associated with such suffered complexities, performing valid process monitoring/fault diagnosis (PM-FD) is becoming a challenging task of ensuring process safety and product quality. In this paper, a new PM-FD approach based on principal component regression (PCR) is proposed for quality-relevant fault detection and diagnosis of HSMP. Firstly, the historical multi-batch process and quality variables data sets (three-dimensional) should be appropriately transferred into the availably applied two-dimensional data sets. Then, the presented approach could orthogonally project the process variable space into the quality-relevant part from the -irrelevant part. Next, when there comes a new measurement regarding a new batch from an unknown specification of the strip, it is automatically assigned into its preferential model by the prediction power of PCR integrated with Bayes inference. In depth, the detection and diagnosis are continued by the presented PCR based monitoring scheme. To the end, the new proposed scheme would be practiced with real HSMP data, where the individual steps as well as the complete framework were extensively tested.

Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill

This paper is focused on the development of the quality prediction and fault detection schemes for the industrial hot strip mill process (HSMP). Considering that the pure partial least squares (PLS) model is based on the assumption of a single operating mode, in this paper, a multiple PLS based method is proposed. The new method address the multi-mode problem in HSMP with the Gaussian mixture model (GMM), then the advantage of original PLS is subsequently followed to achieve the quality prediction and monitoring goals. Meanwhile, a new probabilistic fault detection index called quality-related fault probability index is also developed for the fault detection purpose. Finally, the whole proposed scheme is exercised with the real industrial data, and performances are evaluated by comparing with other existing methods.

Quality-related fault detection using linear and nonlinear principal component regression

Abstract The issue of quality-related fault detection is a hot research topic in the process monitoring community in the recent five years. Several modifications based on partial least squares (PLS) have been proposed to solve the relevant problems for linear systems. For the systems with nonlinear characteristics, some modified algorithms based on kernel partial least squares (KPLS) have also been designed very recently. However, most of the existing methods suffer from the defect that their performances are not stable when the fault intensity increases. More importantly, there is no way yet to solve the linear and nonlinear problems in a uniform algorithm structure, which is very important for simplifying the design steps of fault detection systems. This paper aims to propose such approaches based on principal component regression (PCR) and kernel principal component regression (KPCR). Such that, relevant problems in linear and nonlinear systems can be solved in the same way. Two literature examples are used to test the performance of the proposed approaches.

A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process

Hot strip mill process (HSMP) plays a pivotal role in steel manufacturing industry, but involves significant complexity. Several faults could cause the decreasing evaluation of the key performance indicators (KPIs). Partial least squares (PLS) model has been popularly accepted for KPI-monitoring tasks, whereas some drawbacks have been reported such as high false alarm rate and strict limitation of Gaussian distribution. In this paper, a new scheme is designed without any distributional priority. The process information is extracted by the independent component analysis (ICA) and principal component analysis (PCA) one after another to obtain the Non-Gaussianity and Gaussianity rooted in process variables. Then the correlation canonical analysis (CCA), a classic tool of analyzing the correlation of two data sets, will be utilized to incorporate the process information and KPIs. Finally, two KPI-related indices are formed respectively, which are both bounded by key density estimation based approach. In the end, application of the new approach in a real steel plant will be demonstrated, where the comparison with PLS based results is covered.

Assessment of T2- and Q-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring

Abstract The pioneering multivariate statistical process monitoring (MSPM) methods use the Q-statistic as an alternative for the T2-statistic to detect faults occurring in the residual subspace spanned by the process variables, since directly using T2 for this subspace can lead to numerical problems. Such use has also spread to current work in MSPM field. However, substantial improvement of computational resource has sufficiently mitigated the numerical problem, which, thus, leads to a need to assess their detectability when using in the same position. This paper seeks to solve this historical issue by examining the two statistics in light of the fault detection rate (FDR) index to assess their performance when detecting both additive and multiplicative faults. Theoretical and simulation results show that the two statistics have different impacts on computing the FDR. Furthermore, it is shown that, the T2-statistic performs, in terms of the FDR, better at detecting most additive and multiplicative faults. Finally, based on the achieved results, a remedy to the interpretation of traditional MSPM methods are given.

Event-triggered fault detection framework based on subspace identification method for the networked control systems

In this paper, the subspace identification method (SIM) based event-triggered fault detection (FD) is put forward to deal with the process monitoring of networked control systems (NCSs). The core concept is to construct a SIM based event-triggered residual generator. For this purpose, a parity space based residual generator is firstly established directly from test data, instead of the process model. Moreover, the event-triggered strategy is introduced to obtain an event-triggered residual generator, which is of great efficiency to reduce data transmission and guarantee the fault detection accuracy simultaneously. Finally, the application of the proposed method is illustrated by the computer control system of hot strip mill process (HSMP).