Hidden Markov Model-Based Statistics Pattern Analysis for Multimode Process Monitoring: An Index-Switching Scheme

Multiple operating modes pose a challenge for process monitoring in industry. Although many monitoring approaches have achieved quite success, most of them neglected the dependency of sampled data and only dealt with samples in a separate fashion. This paper proposes a sequential framework for multimode process monitoring with hidden Markov model-based statistics pattern analysis (HMM-SPA). To begin with, a hidden Markov model is trained on the basis of the historical data. Statistics pattern analysis mixture models (SPAMM) are constructed to characterize the distinctive statistical pattern of each operating mode. Then, during online monitoring period, the mode vector is obtained using the Viterbi algorithm, and the differential mode vector is calculated. At last, the proposed method switches to an appropriate monitoring index automatically, according to the norm of the differential mode vector. The effectiveness of the proposed method is demonstrated by a numerical simulation, a continuous stirred tank heater (CSTH) process, and the Tennessee Eastman process

Incipient Sensor Fault Diagnosis Using Moving Window Reconstruction-Based Contribution

Reconstruction-based contribution (RBC) is widely used for fault isolation and estimation in conjunction with principal component analysis (PCA)-based fault detection. Correct isolation can be guaranteed by RBC for single-sensor faults with large magnitudes. However, the incipient sensor fault diagnosis problem is not well handled by traditional PCA and RBC methods. In this paper, the limitations of traditional PCA and RBC methods for incipient sensor fault diagnosis are illustrated and analyzed. Through the introduction of a moving window, a new strategy based on the PCA model is presented for incipient fault detection. Regarding incipient fault isolation and estimation, a new contribution analysis method called moving window RBC is proposed to enhance the isolation performance and estimation accuracy. Rigorous fault detectability and isolability analyses of the proposed methods are provided. In addition, effects of the window width on fault detection, isolation, and estimation are discussed. Simulation studies on a numerical example and a continuous stirred tank reactor process are used to demonstrate the effectiveness of the proposed methods

Joint state and fault estimation of complex networks under measurement saturations and stochastic nonlinearities

In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously. In the presence of measurement saturations and stochastic nonlinearities, upper bounds of the error covariances of the fault estimates are recursively obtained and then minimized. Sufficient conditions are proposed to guarantee the existence and the unbiasedness of the developed estimator. Our developed estimator design algorithm is distributed because it depends only on the local information and the information from the neighboring subsystems, thereby avoiding the usage of a center estimator. Finally, simulation results are presented to show the performance of the proposed strategy in simultaneously estimating the states and faults