IEEE Industrial Electronics Society
IEEE Industrial Electronics Society
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Guest Editorial: Special Section on Industrial Control
Weng Khuen Ho and S. Joe Qin
1. Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares
Yingwei Zhang , Hong Zhou, S. Joe Qin, and Tianyou Chai
Abstract: In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.
2. Data-driven Soft Sensor Approach for Quality Prediction in a Refining Process
Wang, David; Srinivasan, Rajagopalan; Liu, Jun
Abstract: In petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. On-line prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this article, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements.
3. Fault Detection based on Statistical Multivariate Analysis and Microarray-type Visualization
Ming-Da Ma, David Shan-Hill Wong, Shi-Shang Jang and and Sheng-Tsaing Tseng
Abstract: In this work, a statistical method is proposed to mine out key variables from a large set of variables recorded in a limited number of runs through a multi-stage multi-step manufacturing process. The method employed well-known single variable or multivariable techniques of discrimination and regression but also presented a synopsis of analysis results in a colored map of p-values very similar to a DNA microarray. This framework provides a systematic method of drawing inferences from the available evidence without interrupting the normal process operation. The proposed concept is illustrated by two industrial examples.
4. Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
Pabara-Ebiere P. Odiowei, Yi Cao
5. Bidirectional Branch and Bound for Controlled Variable Selection Part III: Local Average Loss Minimization
Kariwala, Vinay; Cao, Yi
Abstract: The selection of controlled variables (CVs) from available measurements through exhaustive search is computationally forbidding for large-scale processes. We have recently proposed novel bidirectional branch and bound (B3) approaches for CV selection using the minimum singular value (MSV) rule and the local worst-case loss criterion in the framework of self-optimizing control. However, the MSV rule is approximate and worst-case scenario may not occur frequently in practice. Thus, CV selection by minimizing local average loss can be deemed as most reliable. In this work, the B3 approach is extended to CV selection based on local average loss metric. Lower bounds on local average loss and, fast pruning and branching algorithms are derived for the efficient B3 algorithm. Random matrices and binary distillation column case study are used to demonstrate the computational efficiency of the proposed method.
6. Kalman Predictive Redundancy System for Fault Tolerance of Safety-critical Systems
Lee, Kyung Chang; Kim, Man Ho; Lee, Suk
Abstract: The dependence of intelligent vehicles on electronic devices is rapidly increasing the concern over fault tolerance due to safety issues. For example, an x-by-wire system, such as electromechanical brake system in which rigid mechanical components are replaced with dynamically configurable electronic elements, should be fault-tolerant because a critical failure could arise without warning. Therefore, in order to guarantee the reliability of safety-critical systems, fault-tolerant functions have been studied in detail. This paper presents a Kalman predictive redundancy system with a fault-detection algorithm using the Kalman filter that can remove the effect of faults. This paper also describes the detailed implementation of such a system using an embedded microcontroller to demonstrate that the Kalman predictive redundancy system outperforms well-known average and median voters. The experimental results show that the Kalman predictive redundancy system can ensure the fault-tolerance of safety-critical systems such as x-by-wire systems.
7. H-infinity State Feedback Control for a Class of Networked Cascade Control Systems with Uncertain Delay
Congzhi Huang, Yan Bai, Xiangjie Liu
Abstract: Based on practical industrial process control, a typical configuration for networked cascade control systems (NCCSs) is analyzed. This kind of NCCSs with state feedback controllers, in which the network-induced delay is uncertain and less than a sampling period, is studied. The sufficient condition for the stabilizability of the NCCSs without disturbances is proposed, and the state feedback stabilization control laws are derived via Lyapunov stability theory and linear matrix inequality(LMI) approach. For the NCCSs with disturbances, the criterion of its robust asymptotically stability is derived and the γ-suboptimal state feedback H∞ control laws are designed. The γ-optimal state feedback H∞ control laws are also put forward by optimizing a set of LMIs. A simulation example of a networked cascade control system for the main steam temperature in a power plant is given to demonstrate the effectiveness of the proposed approaches.
8. Design the Remote Control System with the Time-Delay Estimator and the Adaptive Smith Predictor
Lai, Chien-Liang; Hsu, Pau-Lo
Abstract: In real applications, a remote control system is generally an integration of different networks consisting of a commercial network for message transmission and an industrial network to control the remote hardware through a communication gateway. Since the induced time delay in network control system (NCS) may cause system instability, this paper proposes a remote NCS structure by implementing the adaptive Smith predictor with an on-line time-delay estimator. As the delay in a commercial network Ethernet is significantly time-varying depending on the number of end-users, the delay is estimated in this paper by processing the on-line measurement of the round-trip time (RTT) between the application layers of the server and the client. The adaptive Smith predictor control scheme is developed by directly applying the estimated time delay. To prove the feasibility of the proposed remote control system, the developed design has been applied to an AC 400 W servo motor tested from a 15 km distance. The experimental results indicate that the significantly improved stability and motion accuracy can be reliably achieved by applying the proposed approach.
9. A Hybrid FLC-EKF Scheme for Temperature Control of a Refinery Debutanizer Column
Jana, Amiya
Abstract:A nonlinear feedback linearizing control (FLC) strategy is proposed within the differential geometric framework for temperature control of a refinery debutanizer column. The distillation model is verified by real data. The FLC control algorithm usually consists of a transformer, a state estimator and an external linear controller. Here, two state estimators, namely extended Kalman filter (EKF) and short-cut model-based open-loop estimator (SMBOLE), have been developed to device the hybrid FLC-EKF and FLC-SMBOLE control systems, respectively. In order to avoid estimator design complexity as well as computational burden, an ideal binary distillation model (light key (LK)/heavy key (HK)) has been used as an EKF predictor and open-loop estimator (OLE). In this article, a comparative study has been conducted between the FLC-EKF, FLC-SMBOLE and a classical dual-loop proportional integral derivative (PID) control structure. Simulation results show that despite the significant process/model mismatch, the proposed FLC controllers perform better than the PID control scheme.
10. Flexible On-Board Stream Processing for Automotive Sensor Data
Schweppe, Hendrik; Zimmermann, Armin; Grill, Daniel
Abstract:Vehicle testing and diagnosis requires huge amounts of data to be gathered and analyzed. Not all possibly interesting data can be stored because of the limited memory available in a tested vehicle. On-board preprocessing of data and decisions about which information has to be kept or omitted is thus vital for vehicle testing routines. This paper introduces a method for flexible on-board processing of sensor data of a vehicle. The approach is motivated by sensor network ideas and makes use of stream processing techniques. A processing graph model for automotive applications is proposed, which consists of operator nodes and connecting data streams. This model supplies both recording and processing functionality together. To account for dynamic changes of conditions within a vehicle, both the model and actual software are built in such a way, that the whole system can automatically be adapted at runtime whenever certain conditions are detected. The proposed stream processing model has been implemented in a proof-of-concept industrial application, that was deployed to an automotive on-board unit. Results show that this approach effectively trades a little more on-board processing power for a large data volume, that does not need to be saved and transmitted for off-board usage anymore.
11. Overrun Methods and Resource Holding Times for Hierarchical Scheduling of Semi-Independent Real-Time Systems
Behnam, Moris; Nolte, Thomas; Sjödin, Mikael; Shin, Insik
The Hierarchical Scheduling Framework (HSF) has been introduced as a design-time framework to enable compositional schedulability analysis of embedded software systems with real-time properties. In this paper a software system consists of a number of semi-independent components called subsystems. Subsystems are developed independently and later integrated to form a system. To support this design process, in the paper, the proposed methods allow non-intrusive configuration and tuning of subsystem timing-behaviour via subsystem interfaces for selecting scheduling parameters. This paper considers three methods to handle overruns due to resource sharing between subsystems in the HSF. For each one of these three overrun methods corresponding scheduling algorithms and associated schedulability analysis are presented together with analysis that shows under what circumstances one or the other is preferred. The analysis is generalized to allow for both Fixed Priority Scheduling (FPS) and Earliest Deadline First (EDF) scheduling. Also, a further contribution of the paper is the technique of calculating resource-holding times within the framework under different scheduling algorithms; the resource holding times being an important parameter in the global schedulability analysis.
12. A Novel Anti-collision Algorithm in RFID Systems for Identifying Passive Tags
Horng, Shi-Jinn; Chen, Yuan-Hsin; Run, Ray-Shine; Lai, Jui-Lin; Chen, Rong-Jian; Chen, Wei-Chih; Pan, Yi; Terano, Takao
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