Identification of faulty sensors using principal component analysis

1996/10/01 by Ricardo Dunia, S. Joe Qin, Thomas F. Edgar +1 · 525 citations
Chemistry · Engineering · #Actuator #Artificial intelligence #Computer science #Control (management) #Control theory (sociology) #Data mining #Engineering #Fault (geology) #Fault Detection and Control Systems #Fault detection and isolation #Identification (biology) #Mineral Processing and Grinding #Principal component analysis #Spectroscopy and Chemometric Analyses

paper · doi:10.1002/aic.690421011

published in AIChE Journal 42(10), 2797-2812 (Wiley)

openalex publication_date 1996/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Abstract

Abstract Even though there has been a recent interest in the use of principal component analysis (PCA) for sensor fault detection and identification, few identification schemes for faulty sensors have considered the possibility of an abnormal operating condition of the plant. This article presents the use of PCA for sensor fault identification via reconstruction. The principal component model captures measurement correlations and reconstructs each variable by using iterative substitution and optimization. The transient behavior of a number of sensor faults in various types of residuals is analyzed. A sensor validity index (SVI) is proposed to determine the status of each sensor. On‐line implementation of the SVI is examined for different types of sensor faults. The way the index is filtered represents an important tuning parameter for sensor fault identification. An example using boiler process data demonstrates attractive features of the SVI.

Cited by

• Reconstruction-based contribution for process monitoring
• Nonlinear process monitoring using kernel principal component analysis
• A review of process fault detection and diagnosis
• A review of process fault detection and diagnosis
• Gross error modeling and detection in plant linear dynamic reconciliation
• A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems
• Data-driven Fault Detection and Diagnosis for HVAC water chillers
• Detecting Differences Between Correlation-Matrix Populations due to\n Single-variable Perturbations, with Application to Resting State fMRI
• Weighted and constrained possibilistic C-means clustering for online fault detection and isolation
• Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis
• Statistical process monitoring: basics and beyond
• Inspection of Piezoceramic Transducers Used for Structural Health Monitoring. [europepmc]
• Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach. [europepmc]
• Multi-Features Fusion for Fault Diagnosis of Pedal Robot Using Time-Speed Signals. [europepmc]
• Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review. [europepmc]

Related

• Fault Detection and Identification Using a Novel Process Decomposition Algorithm for Distributed Process Monitoring
• A Probabilistic Model For Sensor Validation
• Identification of combined sensor faults in structural health monitoring systems
• Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
• Direct identification of fault estimation filter for sensor faults