Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis
A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationFault DetectionGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks
Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the pe…
Fault DetectionFault DiagnosisGeneral ClassificationHierarchical Fault Detection and Diagnosis for Transformer Architectures
Transformers now underpin critical AI systems across industry and research. Yet their faults can silently alter model behavior without runtime errors, and existing techniques offer little support for tracing these failur…
Fault DiagnosisAutomatic Channel Fault Detection and Diagnosis System for a Small Animal APD-Based Digital PET Scanner
Fault detection and diagnosis is critical to many applications in order to ensure proper operation and performance over time. Positron emission tomography (PET) systems that require regular calibrations by qualified scan…
Fault DetectionFault DiagnosisResidual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis
Data-driven fault diagnosis is complicated by unknown fault classes and limited training data from different fault realizations. In these situations, conventional multi-class classification approaches are not suitable fo…
Anomaly ClassificationBIG-bench Machine LearningClassificationFault Diagnosis+2Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark
In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability. To comb…
Anomaly Detection