Table of Content

29 June 2015, Volume 49 Issue 06 Previous Issue    Next Issue

Automation Technique, Computer Technology

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Quality Monitoring of Nonlinear Process Based on Kernel Projection to Quality Latent Structure HU Jing1，WANG Chunxia2，WEN Chenglin2，LI Ping1 2015, 49 (06):  737-742.  Abstract ( 706 )   Save Abstract： A novel qualityrelated kernel latent structure projection method was proposed and qualityrelated monitoring was applied in the nonlinear process. Different to the KPLS, the proposed method further decomposes the quality variable space into three subspaces, which can achieve a complete monitoring of the quality which can be and cannot be interpreted by process quality. Meanwhile, the validity of the proposed method was verified by TE simulation. Key words： kernel projection to quality latent structure; nonlinear process; quality monitoring References | Related Articles | Metrics

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Composite Iterative Learning FaultTolerant Guaranteed Cost Controller Design for Batch Process WANG Limin1，2,ZHOU Donghua1,ZHU Chengjie2 2015, 49 (06):  743-750.  Abstract ( 648 )   Save Abstract： Focusing on the batch processes with actuator fault and state delay, a compound iterative learning reliable guaranteed cost controller combining feedback control and iterative learning control was proposed. The fault system with time delay was transformed to the delay system with the equivalent 2DFM model. Then, based on the equivalent model, to guarantee the realization of the system, a robust iterative learning reliable guaranteed cost method based on 2D dynamic output feedback was proposed with developed learning information. Timedelaydependent sufficient conditions guaranteeing the system stability and best performance was constructed. Furthermore, an optimal design algorithm with expanded learning information of robust iterative faulttolerant guaranteed cost control was derived, while taking the influence of time delay on system stability and performance into consideration. The closedloop system could run smoothly within the fault permissible range when actuator failure occurred. The application on the nozzle pressure in packing phase of injecting modeling process proves the effectiveness of the proposed scheme. Key words： batch process; iterative learning; faulttolerant guaranteed cost control; actuator fault; delay dependent References | Related Articles | Metrics

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Vibration-Related Fault Diagnosis in Cold Rolling Mill by Using EEMD and SVM YANG Xu1,PENG Kaixiang1,LUO Hao2,KRUEGER Minjia2,ZONG Dazi1,DING Steven X2 2015, 49 (06):  751-756.  Abstract ( 703 )   Save Abstract： By analyzing the vibration process of cold rolling and using the structure model of the rolling system, a dynamic rolling force of the rolling vertical system was built, with the consideration of the influence of rolling vibration. A data-driven fault diagnosis was proposed based on industrial field data by using ensemble empirical mode decomposition (EEMD) and support vector machine (SVM), with the focus on the generalized fault, which were mostly caused by variations of process parameters under complex working conditions. According to the decoupling effect on measured rolling force data with the EEMD algorithm, the intrinsic mode function (IMF) component was defined as fault eigenvector and chosen as the input into the classifier of vector machine. Then, the vibration-related fault of cold rolling mills could be diagnosed by  the distinction between the normal state and the fault state by SVM. References | Related Articles | Metrics

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Fault Detection for a Class of Closed-Loop Systems with Distributed Measurements LIU Yang,HE Xiao,ZHOU Donghua 2015, 49 (06):  757-761.  Abstract ( 671 )   Save Abstract： The fault detection problem for a class of closed-loop systems with distributed measurements was considered in the paper. The unmodeled dynamics was taken into account, and thus the feedback control would directly influence the residual signal and the fault detection performance. As a result, the feedback control law was considered in the design of the proposed residual generator. The addressed systems were monitored by multiple distributed sensors, each of which could communicate with its neighboring nodes. In the presence of the unmodeled dynamics and distributed measurements, a least mean-square distributed residual generator was designed, where each node could obtain the residual signals and detect the possible faults based on not only local measurements but also measurements transmitted from its neighboring nodes. The algorithm could be carried out in a recursive way and thus is suitable for online application. A numerical example was provided to show the effectiveness of the proposed strategy. References | Related Articles | Metrics

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Multi-Rate Principle Component Analysis for Process Monitoring CONG Ya,GE Zhiqiang,SONG Zhihuan 2015, 49 (06):  762-767.  Abstract ( 726 )   Save Abstract： To monitor multi-rate processes, a multi-rate principle components analysis algorithm was proposed in which the covariance matrix was calculated using the incomplete multi-rate data samples. To avoid the bias of the covariance matrix, the resampling method was adopted. Besides, the offline modeling strategy and online monitoring strategy were proposed. Then two case studies on both numerical and Tennessee Eastman process (TEP) simulation was given to prove the effectiveness of the proposed algorithm compared to other methods. The result shows that the proposed method has a better performance in multirate process monitoring. References | Related Articles | Metrics

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A Fault Estimation Method Based on Robust Residual Generators QIN Liguoa,HE Xiaoa,b,ZHOU Donghuaa,b 2015, 49 (06):  768-774.  Abstract ( 711 )   Save Abstract： This paper proposed a fault estimation method based on robust residual generators for a linear system. A system with additive actuator or component faults was considered in the case where the number of the independent faults was larger than that of the independent measurements. This method was achieved based on robust residual generators and there was no need to design extra fault estimators. In this method, fault estimation was achieved via three steps. First, coding sets, which describe the sensitivity relationship between faults and generators, are designed. Second, a bank of robust residual generators are designed according to the coding sets. Finally, fault estimation is achieved by using the result of fault isolation and the output of robust residual generators. A sufficient condition on the application of the method was given and the asymptotic convergence property of the estimation error by using the method was proved. Simulation results demonstrate the effectiveness of the method. References | Related Articles | Metrics

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A Fault Detection Method of Non-Gaussian Processes and Small Shift GUO Tianxu，CHEN Maoyin，ZHOU Donghua 2015, 49 (06):  775-779.  Abstract ( 1050 )   Save Abstract： In this paper, the fault detection of non-Gaussian processes was discussed. A modified sparse representationbased fault detection method was proposed. Two control thresholds  called reconstruction error control threshold (CLE) and distance control threshold (CLDint) were introduced, and the fault detection level was improved. Additionally, by introducing a time constant, a sparse representation-based small shift detection method was proposed based on the same framework above， enhancing the detection ability of small shift. Two numerical examples demonstrate the effectiveness of these two methods, and a comparison between the proposed methods and the classic PCA-based fault detection method shows that the proposed methods are superior. References | Related Articles | Metrics

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Fault Reconstruction for Multiple Failure Modes Based on Threshold Fault Subspace Extraction Algorithm NING Chao,CHEN Maoyin,ZHOU Donghua 2015, 49 (06):  780-785.  Abstract ( 787 )   Save Abstract： This paper considered the fault detection and reconstruction problem for multiple types of sensor failures in a unified framework. The multiple failure modes pose a challenge to performance monitoring and fault reconstruction. First, a fault model was proposed to represent the existence of additive and multiplicative sensor faults in a single framework. From a data-driven perspective, both necessary and sufficient conditions for fault detectability were derived using Rayleigh-Ritz lemma. Additionally, a data-driven fault reconstruction approach based on threshold fault subspace extraction algorithm was proposed. The proposed fault reconstruction method demonstrates superiority over the traditional method in terms of reconstruction errors. Numerical simulations fully verify the main results. References | Related Articles | Metrics

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Advanced Fault-Tolerant Control Design with Consideration of Reconfiguration Switch Transients YU Xiang1,ZHANG Youmin2,1 2015, 49 (06):  786-792.  Abstract ( 863 )   Save Abstract： With the best use of the configured actuator redundancy in a system, a novel fault-tolerant control (FTC) strategy with explicit consideration of reconfiguration switching transients was presented to accommodate actuator failures. Based on the established target model, a bumpless adaptation mechanism was integrated with the reconfigurable control to smoothen the reconfiguration process and further ensure the safety of the post-fault system. Case studies of a benchmark aircraft subject to actuator failures were conducted to demonstrate the effectiveness of the proposed FTC approach. References | Related Articles | Metrics

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Fault Diagnosis of Robots Based on Multi-Sensor Information Fusion WANG Xiuqing1,HOU Zengguang2,ZENG Hui3,L Feng1,PAN Shiying1 2015, 49 (06):  793-798.  Abstract ( 1058 )   Save Abstract： A novel multi-sensor information fusion method  combined with the support vector machine (SVM) was proposed  in diagnosing three types of faults  which are collision, front collision and obstruction, as the robot’s arm approaches the grasping place.   After fusing the proper number of the data from multisensors and searching the optimal parameters C and γ of the SVM by grid searching, the proposed method can successfully diagnose the faults of obstruction, front collision and collision. Besides,  the selection of the number of the features of data to be fused by multisensor information fusion was discussed. The experimental results show that the selection of the proper number of the fusing features of the sampling data influences the number of fusion data obtained and the accuracy of classification. References | Related Articles | Metrics

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Incipient Fault Detection Using Transformed Component Statistical Analysis SAHNG Jun,CHEN Maoyin,ZHOU Donghua 2015, 49 (06):  799-805.  Abstract ( 685 )   Save Abstract： Incipient fault detection is of significant importance for preventing the occurrence of accidents. A multivariate analysis method named transformed component statistical analysis (TCSA) was proposed to solve the incipient fault detection problem. The algorithm processes the data in the sliding time window to extract transformed components. Statistics (mean, variance, skewness, kurtosis) of transformed components were monitored to realize the detection of incipient faults. The transformed components extracted by the approach are linear combinations of the normalized data. Statistics of transformed components can reflect some invariants under normal condition. Some incipient faults break the balance and therefore can be detected. Numerical simulation and Tennessee Eastman process (TEP) simulation indicate that TCSA is able to detect both incipient sensor faults and process faults effectively. References | Related Articles | Metrics

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Fault-Tolerant Consensus for a Network of Multi-Agent Systems with Actuator Faults ZHANG Xu,CHEN Maoyin,WANG Ling，ZHOU Donghua 2015, 49 (06):  806-811.  Abstract ( 796 )   Save Abstract： A fault tolerant consensus problem for multi-agent systems with actuator faults and exterior disturbance was investigated, where the actuator fault was characterized by gain variation. An adaptive-gain compensating control law was added to the nominal control input to ensure consensus. Relative output information rather than state information was utilized to design the control law. By analyzing the stability of the closed-loop tracking error systems, a sufficient condition for consensus of the original system was proposed. It is shown that as long as the control input of leader agent and the exterior disturbance are bounded by some positive constants, fault-tolerant consensus can be realized by appropriately choosing the design parameters. A numerical simulation validates the main results. References | Related Articles | Metrics

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Robust Detection of Intermittent Faults of Linear Discrete-Time Stochastic Systems YAN Rongyia,HE Xiaoa,b,ZHOU Donghuaa,b 2015, 49 (06):  812-818.  Abstract ( 669 )   Save Abstract： In this paper, a robust detection method for intermittent faults (IFs) was proposed for a class of linear discrete-time stochastic systems, subject to unknown disturbances, process noises and measurement noises. A reduced-order unknown input observer was designed and a novel truncated residual was constructed by introducing a sliding time window, which is decoupled from the unknown disturbances, but sensitive to the IF. Two hypothesis tests were put forward to detect all the appearing time and the disappearing time of the IF, respectively. In addition, for the given false alarm rate and the missing detection rate, the detection thresholds for the appearing time and the disappearing time of the IF were rigorously designed for desired performances, respectively， and the detectability of the IF was also analyzed in a probabilistic sense. The simulation results on a satellite model were presented to verify the proposed method. The results illustrate that both the appearing time and the disappearing time of the IF can be detected fast and validly in spite of unknown disturbances. References | Related Articles | Metrics

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Cooperative Fault Tolerant Control for Multi-Vehicle Systems SHI Jiantao,HE Xiao,ZHOU Donghua 2015, 49 (06):  819-824.  Abstract ( 728 )   Save Abstract： In this paper, the finitehorizon mixed H∞/H2 cooperative fault tolerant control problem was investigated for a class of stochastic timevarying multivehicle systems subject to actuator faults. By solving two coupled backward recursive Riccati equations, a distributed cooperative controller was obtained to make the cooperation tracking errors satisfy a given mixed H∞/H2 performance constraints. Effectiveness of the developed strategy was demonstrated by a numerical simulation. References | Related Articles | Metrics

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Fault Diagnosis Algorithm of Based Feature Subspace Estimation in Small Sample Circumstance HOU Yandong,YAN Zhiyu,JIN Yong 2015, 49 (06):  825-829.  Abstract ( 769 )   Save Abstract： To solve the problem that a robust covariance matrix cannot be obtained because of insufficient samples in principal component analysis, the covariance matrix was transformed into the feature subspace estimation problem by introducing the idea of CS decomposition Bayesian spatial estimation. First, the SPE (squard prediction error) statistic threshold and failure mode feature subspace matrix library were established using a large number of historical data using PCA offline. When there exists an abnormal condition in the online system, only a small sample of failure data can be obtained due to the effect of a certain environment. However, the feature subspace matrix can be obtained using a small sample data. Then, fault diagnosis was completed by comparing the similarity between the feature subspace and the failure mode subspace. Finally, the feasibility and effectiveness of this method was verified by simulation. References | Related Articles | Metrics

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k-Nearest Neighbor Imputation Method and Its Application in Fault Diagnosis of Industrial Process LI Yuan1,WU Jie1,WANG Guozhu2 2015, 49 (06):  830-836.  Abstract ( 878 )   Save Abstract： Aimed at  the smearing effect in contribution plot method and the falt that fault variables cannot be located, this paper proposed a kNN imputation method for fault diagnosis, combining k-nearest neighbor and the contribution plot algorithm. First, PCA was adopted to build an evaluation model and calculate the combined index. Secondly, knearest neighbor imputation method and the control index were combined to extract preliminary faulty variables. Finally, the contribution plots were employed to find the fundamental faulty variables from the preliminary faults. The proposed method can avoid the influence of contribution values of normal variables effectively. A numerical example and Tennessee Eastman (TE) process were given to verify the effectiveness and accuracy of the proposed method, compared with the reconstruction-based method. References | Related Articles | Metrics

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Terminal Sliding Mode Observer Based Fault Reconstruction for Underwater Vehicle Thruster CHU Zhenzhong1,ZHU Daqi1,ZHANG Mingjun2 2015, 49 (06):  837-841.  Abstract ( 732 )   Save Abstract： A fault reconstruction method based on terminal sliding mode observer was proposed in this paper. In the traditional sliding mode observer, the estimated error of unmeasured state is asymptotic convergence, which affects the timeliness of thruster fault reconstruction. In response to the above problem, the property of finite time convergence of terminal sliding mode was used to build a terminal sliding mode observer to ensure that all of the state estimation errors converge in a finite time. Then the thruster fault was reconstructed by using the equivalent output error infection method. Finally, the proposed method was validated by simulation. References | Related Articles | Metrics

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Fault Detection Techniques Based on Multivariate Statistical Analysis JI Hongquan,HE Xiao,ZHOU Donghua 2015, 49 (06):  842-848.  Abstract ( 998 )   Save Abstract： As an important branch of data-driven fault detection methods, multivariate statistical analysis-based fault detection methods mainly include principal component analysis, partial least squares, independent component analysis and fisher discriminant analysis. In this paper, the data model and fault detection mechanism of each method mentioned above were reviewed. Several properties of these methods were revealed intuitively using simulation results, and their fault detection abilities were illustrated. Finally, several problems related to data-driven fault detection methods were discussed. References | Related Articles | Metrics

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Fault Diagnosis Based on Particle Filter for Hybrid System LI Xiongjie1,2，ZHOU Donghua2 2015, 49 (06):  849-854.  Abstract ( 664 )   Save Abstract： Aimed at the problem of fault diagnosis in the hybrid system, a particle filter algorithm for hybrid system state estimation and discrete modal identification was proposed. The proposed algorithm was spread to joint estimation of state and parameters, modified Bayes algorithm was utilized to give fault judgment, and fault diagnosis of hybrid system was implemented. The results of the simulation of the twotank state estimation and fault diagnosis show that the proposed algorithm can not only accurately diagnose the hybrid system fault, maintain better state and parameter estimation accuracy when fault occurs, but also be used for adaptive filtering, reliability prediction, fault tolerant control and many other fields. References | Related Articles | Metrics

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Remaining Useful Life Prediction of Nonlinear Stochastic Degrading Systems Subject to Uncertain Measurements SI Xiaosheng1,HU Changhua1,LI Juan2,SUN Guoxi3,ZHANG Qi1 2015, 49 (06):  855-860.  Abstract ( 94 )   Save Abstract： A class of degradation modeling approach was proposed, in which the nonlinear stochastic deterioration and uncertain measurements of the system were considered simultaneously, and the Kalman filtering technique was utilized to estimate the underlying degradation state. Based on the estimated degradation state, the analytical RUL distribution was derived according to the concept of the first passage time which accounted for the uncertainties in the estimated degradation state and measurements, and the effect of the degradation nonlinearity. Additionally, a parameter estimation method for the developed model was presented based on the maximum likelihood method. Finally, a case study of the gyros verified that the proposed method could improve the accuracy of the predicted RUL.   References | Related Articles | Metrics

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An Improved Defogging Algorithm Combined with Edge Detection HUANG Darong,FANG Zhou，ZHAO Ling 2015, 49 (06):  861-867.  Abstract ( 120 )   Save Abstract： The images restored by using the dark channel prior theory have haze edges. Therefore, a novel improved image clearness method based on dark channel prior was proposed to solve this insufficient. First, dark color image was obtained and decomposed by wavelet. Next, the smooth portion  from wavelet decomposition was used as the target image, in which global atmospheric optical A and the dark channel value were calculated, and then, the edge information of target image was extracted and dilated. After that, according to the expanded binary edge image, the dark channel value in edges and that in smooth were optimized, respectively. Finally, according to the transmission map calculated using the optimized dark channel values, the fogfree image was obtained by using the physical model. The experiment results show that this method can effectively restore the fogging images and greatly reduce the time complexity brought by the soft matting algorithm. References | Related Articles | Metrics

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Multimode Process Monitoring Based on Local Neighborhood Standardization Strategy GUO Hongjie1,XU Chunling2,SHI Hongbo1 2015, 49 (06):  868-875.  Abstract ( 79 )   Save Abstract： Complex chemical processes often have multiple operating modes to meet the changes of production condition. The actual industrial processes often contain multiple operating modes and the process data is no longer solely Gaussian or nonGaussian distribution.The multimode characteristics and the uncertainty of data distribution within one single mode make the conventional multivariate statistical process monitoring methods unsuitable for fault detection.To solve the problem of multiple operating modes and complex data distribution, this paper proposed a novel multimode data processing method called local neighborhood standardization (LNS) and local density factor. The LNS was used in data preprocessing and the local density factor was used as a monitoring statistic value. The validity and effectiveness of the proposed method were illustrated through a numerical example and the Tennessee Eastman process. References | Related Articles | Metrics

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Low-Speed Small Target Detection Based on SVD and Superposition GAO Jingli1，WEN Chenglin2，LIU Meiqin1 2015, 49 (06):  876-883.  Abstract ( 162 )   Save Abstract： In the case of the measurement noises obtained from the same pixel position in a set of images which are ergodic, the feasibility of superposition method for target detection was analyzed, based on which, a superposition-based lowspeed small target detection algorithm was proposed. First, the energy variation of the target synthesis images and the noise synthesis images were analysized, when the targets were non-overlapped, completely overlapped and partially overlapped. The analysis shows that with the increase in the superimposed number, the energy of the noise synthesis images is attenuated faster than that of the target synthesis images, thus ensuring an increase in the ratio of target to noise in synthesis images. Second, depending on the characteristics of singular values of the synthesis images, the uneven variations of the standardized singular values were used for target detection. The simulation verifies the attenuation of the target energy and noise energy of the synthesis images, and analyzes the impact of superimposed number， target size and strength on target detectability. References | Related Articles | Metrics

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EKF-Based Fault Detection of Unmanned Aerial Vehicle Flight Control System LIU Xiaodong,ZHONG Maiying,LIU Hai 2015, 49 (06):  884-888.  Abstract ( 139 )   Save Abstract： The unmanned aerial vehicle (UAV) flight control system is a typical multisensor closedloop system. Since actuator faults and sensor faults could seriously affect the security and reliability of the system, fault detection of the UAV flight control system is of great significance. This paper deals with the problem of fault detection of the closed-loop nonlinear model of the UAV flight control system. The nonlinear model of longitudinal motion of the UAV in the presence of wind disturbance was introduced. The extended Kalman filter (EKF) was utilized for the residual generation. The Chisquare test was selected for the residual evaluation and the fault detection task for UAV closedloop system was accomplished. Finally, based on the simulink platform of a certain type of UAV, simulation results are provided to illustrate the effectiveness of the approach proposed in the case of pitut fault and elevator fault. References | Related Articles | Metrics

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Incipient Fault Detection of Linear System with Disturbance LIU Chun,JIANG Bin,ZHANG Ke,WU Yunkai 2015, 49 (06):  889-896.  Abstract ( 78 )   Save Abstract： A synthesized adaptive and sliding-mode observer (SMO) was designed for the incipient fault in a class of linear system with unknown disturbance, which can not only estimate the incipient fault, but also have strong robustness against unknown disturbances. The linear system was transformed by coordinate transformation into two subsystems, one of which was free from disturbances and an adaptive observer is designed to estimate the incipient fault, the other of which was affected by disturbances and incipient faults and a sliding mode observer is designed to eliminate the effect of unknown disturbances and ensure the robustness of the system. Simulation results of the quadrotor helicopter verify the efficiency of the proposed adaptive and sliding-mode observer-based incipient fault detection algorithm. References | Related Articles | Metrics

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Non-Gaussian Information Based JITL Soft Sensor Model LI Yuan,ZHANG Xinmin 2015, 49 (06):  897-901.  Abstract ( 77 )   Save Abstract： In order to monitor the non-Gaussian industrial process, a novel non-Gaussian information based JITL soft sensor model was proposed in this paper. First, the non-Gaussian dissimilarity measure selects the most relevant local modeling samples of JITL model. Then, an ICA-PLS regression method was established on the most relevant local samples for quality variable prediction. From the local relevant sample selection to the final regression model construction, the proposed method can efficiently extract the higher-order statistical information and is well suited for the non-Gaussian process quality prediction. Meanwhile, the proposed method can well cope with the changes in process characteristics as well as nonlinearity. The validity of the proposed method was verified on the sulfur recovery unit. References | Related Articles | Metrics

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A Multi-Agent System Based Fault Diagnostic Approach for Topological Structure of Dynamic Networking System QIU Lu1,YE Yinzhong2 2015, 49 (06):  902-906.  Abstract ( 67 )   Save Abstract： In this paper, an approach in the identification of the dynamic network topology based on the multi-Agent system was proposed. The structure of the fault diagnosis system was established, and a neighbor Agent search algorithm with fault tolerant coordination mechanism was given. The simulation results with a 16 nodes dynamic network system prove that the proposed approach can effectively identify the topological structure of the dynamic network system. References | Related Articles | Metrics

Electrotechnology

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Multi-Carrier Control Strategy for a Three-Level Inverter Tolerant Topology CHEN Danjiang1，YE Yinzhong2 2015, 49 (06):  907-912.  Abstract ( 118 )   Save Abstract： Since multi-level inverter has a lot of advantages over the two-level inverter, it is widely used in high power and other situation. However, its complex topology also brings a relatively large probability of failure problem. In studying on fault diagnosis and tolerant multi-level inverter, a lot of new fault tolerant topologies were proposed. Aimed at a NPC inverter tolerant topology with asymmetrical bridge, control strategies were studied in this paper. This tolerant topology has a two-level bridge and some three-level bridges. It is proposed that the phase between triangular carrier of the twolevel bridge and multi-carrier of three-level bridges will affect the output characteristics of the circuit. When both carriers are in the same phase, the harmonic content of the output voltage waveform is relatively small. The theoretical analysis and experimental results verified the effectiveness of the topology. References | Related Articles | Metrics

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Fault Characteristics  of Microgrid and Protection Strategies ZHAO Junrui,WANG Zhanshan,WANG Jidong,ZHANG Huaguang 2015, 49 (06):  913-922.  Abstract ( 129 )   Save Abstract： A simulation model of microgrid was built, the characteristics of fault current were analyzed, and the existing microgrid protection technologies were reviewed in this paper. First, simulation model of IIDG was built to study fault characteristics and verify the stability of microgrid while different faults occur on the transmission line. Then, the simulation model of microgrid was built to study fault characteristics under islanded mode and gridconnected mode.The result shows that it is necessary to study new protection strategies which do not depend on the fault current effective value. After that, the latest research progresses of microgrid protection technologies were summarized and the advantages and disadvantages of various protection technologies were analyzed. Finally, the research direction and the research objective were prospected, pointing out that the extensiveness of information collection and the integration of protection function were the aims of microgrid protection. Key words： References | Related Articles | Metrics

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Fault Diagnostic System for Chillers Based on Characteristic Petri Net Modeling XU Bing1,2,ZHU Yacheng2,SU Jun2，FAN Qiumin2 2015, 49 (06):  923-928.  Abstract ( 88 )   Save Abstract： The chillers are important in air-conditioning systems, whose operational status is directly related to the HVAC system performance and stability buildings. The fault diagnose of chillers has received wide attention.This paper discussed characteristic modeling technology in fault diagnosis for chillers with fuzzy Petri net modling technique.Using fuzzy Petri net to represente knowledge and inference algorithm for matrix operations, the characteristic parameters of chiller fault indication and relationships was discribed. Using the proposed diagnostic systems, the information resources of characteristic parameters can be used for fault diagnosis and the chiller efficiency indexes of COP can be used for comprehensive diagnosis of fault diagnosis. The test results of plant running show the feasibility and accuracy of the fault diagnostic system. References | Related Articles | Metrics