A framework for Case-Based Diagnosis of batch processes in the principal components space
- Resource Type
- Conference
- Authors
- Berjaga, Xavier; Pallares, Alvaro; Melendez, Joaquim
- Source
- 2009 IEEE Conference on Emerging Technologies & Factory Automation Emerging Technologies & Factory Automation, 2009. ETFA 2009. IEEE Conference on. :1-9 Sep, 2009
- Subject
- Power, Energy and Industry Applications
General Topics for Engineers
Fault detection
Fault diagnosis
Power quality
Monitoring
Injection molding
Principal component analysis
Testing
Wastewater treatment
Process control
Sufficient conditions
- Language
- ISSN
- 1946-0740
1946-0759
This paper presents a framework for fault detection and diagnosis of batch processes based on the information directly gathered from sensors. First, a statistical model of the process is build using Multiway Principal Component Analysis (MPCA) for dimensionality reduction and fault detection tasks. Afterwards, a Case-Based Reasoning (CBR) approach is used for fault diagnosis and for false alarm and missed detection reduction. This framework has been tested in two completely different fields: Power Quality Monitoring for relative location of voltage sags and Injection Moulding Processes for faulty sensor detection and diagnosis. Results obtained show that this framework presents a good performance and is general enough to be applied to any field, if the appropriate preprocess of the data is carried.