Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Arunthavanathan, Rajeevan; Khan, Faisal; Ahmed, Salim; Imtiaz, Syed

doi:10.1021/acs.iecr.1c02731

Cited by 46 publications

(12 citation statements)

References 39 publications

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“…The aim of using ANN is to mode identification when real-time data come. ANN and its variances have ample applications in process monitoring. ,, Interested readers are referred to refs − to know how they can be applied to solve classification problems (e.g., fault diagnosis and mode identification). For each operating mode, the following substeps are performed.…”

Section: Methodology For Dynamic Process Safety Assessmentmentioning

confidence: 99%

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Amin

Khan

2022

Ind. Eng. Chem. Res.

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Fault detection and diagnosis (FDD) is crucial for dynamic process safety analysis. Integrated with failure prediction models, it enables us to realize how a deviation in process variable(s) can affect system safety (measured as risk). This work aims to overcome the challenges of nonlinear, non-Gaussian, and multimodal behavior of the processing systems to detect abnormal process operations, predict dynamic operational risk, and diagnose root cause of the abnormal situation. A methodology is proposed here by integrating different techniques. The artificial neural network (ANN) is used to identify process modes, while the Bayesian network (BN) is used for fault detection. How a fault will lead to a process failure is modeled using the event tree (ET), whereas time-dependent losses associated with the failure scenarios are assessed using the inverted normal loss function (INLF). A probability adaption mechanism is used to estimate the conditional probabilities in each time slice. The complexity of estimating conditional probabilities is handled using the copula theory. The proposed framework is validated using numerical, simulated, and industrial datasets. The results suggest that the developed framework can provide greater flexibility and wider applications.

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Section: Methodology For Dynamic Process Safety Assessmentmentioning

confidence: 99%

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Amin

Khan

2022

Ind. Eng. Chem. Res.

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“…The Tennessee Eastman process (TEP) is widely used as an industrial benchmark for time series data analysis, and fault detection, and diagnosis. − The process flow diagram of TEP is shown in Figure . The plant consists of 5 major process units: an exothermic two-phase reactor, a product stripper, a condenser, a vapor–liquid separator, and a recycle compressor (Downs & Vogel, 1993).…”

Section: Case Studiesmentioning

confidence: 99%

Transfer Entropy-Based Automated Fault Traversal and Root Cause Identification in Complex Nonlinear Industrial Processes

Zope

Singhal

Nistala

et al. 2023

Ind. Eng. Chem. Res.

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Root cause identification (RCI) of faults in industrial processes enables plant operators to pinpoint the source(s) of the fault and take appropriate corrective actions to prevent failures. Conventional techniques for RCI are not particularly suited for causal maps having cycles and time lags that are characteristic of industrial operations. We propose a fault traversal and root cause identification (FTRCI) algorithm for automatic identification of fault traversal pathways and root cause variables from causal maps. Multivariate time delayed transfer entropy between fault variable states is calculated to model the causal dependencies that are represented as a causal map. Then, using FTRCI, the fault traversal paths within the causal map are traced and the corresponding root cause variables are identified. The proposed methodology was applied to complex faults in the Tennessee Eastman process and an industrial coal pulverizer, and the root cause variables identified using this methodology were in accordance with the process knowledge.

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“…5 variables are involved and two conditions described in Table I are considered to test the effectiveness of our proposed SCCAM method. In the Fault 1 condition, a fault is applied in the steam valve position, thus the steam value is the corresponding root cause [25].…”

Section: Case Studymentioning

confidence: 99%

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

Li¹,

Peng²,

Zhang³

et al. 2023

Preprint

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In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a large number of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To address the aforementioned issues, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contribution behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is tested on a continuous stirred tank heater and the Tennessee Eastman industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification

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Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

Cited by 46 publications

References 39 publications

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Transfer Entropy-Based Automated Fault Traversal and Root Cause Identification in Complex Nonlinear Industrial Processes

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

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