Self-Organizing Maps
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Inference in Hidden Markov Models
Inference in Hidden Markov Models
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
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The development of automatic and reliable monitoring systems is an open issue in continuous industrial chemical processes. The challenges lay on simultaneously managing multiple normal modes of operation as well as the transitions among them with reasonable false alarm rates, and in reaching early fault detection. This work explores and attests the capacity of the signal processing method called hidden Markov model (HMM) in contributing to overcome these issues. After presenting the motivation for its use in this engineering field, the methodology is introduced and an application is illustrated. Here, the HMM ability of directly learning from process historical data both desired features system dynamics and structure of correlations is shown. Aiming to reach practical insights a real case study based on operations of an industrial boiler is used. A comparison with Principal Components Analysis (PCA) and Self-Organizing Maps (SOM) shows the effectiveness of the proposed HMM-based fault detection system.