Abstract
This paper presents a novel fault detection approach for industrial batch processes. The batch processes under consideration are characterized by the interaction between discrete system modes and non-stationary continuous dynamics. Therefore, a stochastic hybrid process model (SHPM) is introduced, where process variables are modeled as time-variant Gaussian distributions, which depend on hidden system modes. Transitions between the system modes are assumed to be either autonomous or to be triggered by observable events such as on/off signals. The model parameters are determined from training data using expectation-maximization techniques. A new fault detection algorithm is proposed, which assesses the likelihoods of sensor signals on the basis of the stochastic hybrid process model. Evaluation of the proposed fault detection system has been conducted for a penicillin production process, with the results showing a significant improvement over the existing baseline methods.Note to Practitioners—Automatic fault detection makes it possible to limit the effects of faults by taking countermeasures at an early stage. In this work, a data-driven fault detection method for industrial batch processes is proposed, in which the underlying process model is learned from training data. The proposed fault detection system can be used for various industrial batch processes without the need for complex and error-prone manual configuration. In contrast to many other data-driven approaches such as neural networks, only a few process cycles are required to create a robust process model. It should be noted that in data-driven fault detection methods, the training data should cover a large part of the process states that occur during error-free process cycles. The developed method is therefore particularly suitable for cyclical processes, which, however, can have alternative process paths and variability between the process cycles.
Highlights
T HE main part of industrial production systems are safetycritical systems, which require continuous and reliable operation
Existing Hidden Markov Models (HMMs)- or Segmental HMMs (SHMMs)-based fault detection methods are predominantly focused on modeling of progressive degradations of the plant condition, which are adequately modeled as hidden state sequence with stochastic transitions between failure modes
A substantial novelty of the proposed method is the introduction of a stochastic hybrid process model (SHPM), which allows to model gradually changing system behavior in hidden system states, and, to reduce the number of hidden states compared to HMMs with piecewise stationary emission distributions
Summary
T HE main part of industrial production systems are safetycritical systems, which require continuous and reliable operation. Hybrid automata are the most common approach to represent hybrid systems within the scope of fault detection and isolation (see section II) In this approach it is assumed that the process can be divided into distinct system modes, i.e. phases of continuous process behavior. Existing HMM- or SHMM-based fault detection methods are predominantly focused on modeling of progressive degradations of the plant condition, which are adequately modeled as hidden state sequence with stochastic transitions between failure modes A substantial novelty of the proposed method is the introduction of a stochastic hybrid process model (SHPM), which allows to model gradually changing system behavior in hidden system states, and, to reduce the number of hidden states compared to HMMs with piecewise stationary emission distributions.
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