A Transductive Learning Approach to Process Fault Identification
MetadataShow full item record
The problem of fault identification is considered using recent developments in machine learning that allow the use of unlabeled data to optimally define decision boundaries that separate data belonging to different categories. Traditionally, fault identification uses either hardware redundancy or software redundancy for trouble-shooting the source of faults in a system. Unfortunately, this imposes a data redundancy cost on such systems. Instead of performing model inversion that requires an accurate model, in transduction estimates of the values of a function at specified points are required, instead of learning a general rule on the entire input domain. Transductive learning is motivated from similar arguments underlying state-of-the-art classification and regression methods such as support vector machines. However, transduction is more fundamental as it is a step used in proving learning error bounds in classical statistical learning theory. Use of transduction allows a flexible ordering of the classes of functions from which a model is selected and, therefore, the error bounds are provably tight. The potential of the proposed framework is assessed using data from metallurgical process systems. It is shown that for higher dimensional and large multiclass systems, the proposed framework gives betterperformances with respect to classification error minimization.
Showing items related by title, author, creator and subject.
Rafique, Muhammad T. (2009)The data generated in a chemical industry is a reflection of the process. With the modern computer control systems and data logging facilities, there is an increasing ability to collect large amounts of data. As there are ...
Jemwa, G.; Aldrich, Chris (2005)The behaviour of liquid–liquid extraction systems can be complex and as a result linear methods of process condition monitoring such as principal component analysis or partial least squares may not be able to detect and ...
El-Mowafy, Ahmed ; Wang, Kan; El-Sayed, Hassan (2022)Integrity monitoring (IM) is a vital task for precise real-time positioning in road transportation, autonomous driving, and drones, where safety is essential. IM has the main tasks of detection and exclusion of faulty ...