About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Eng Appl Artif Intell
Paper
LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines
Abstract
Accurate prediction of forthcoming faults in modern industrial machines plays a key role in reducing production arrest, increasing the safety of plant operations, and optimizing manufacturing costs. The most effective condition monitoring techniques are based on the analysis of historical process data. In this paper we show how Least Squares Support Vector Machines (LS-SVMs) can be used effectively for early fault detection in an online fashion. Although LS-SVMs are existing artificial intelligence methods, in this paper the novelty is represented by their successful application to a complex industrial use case, where other approaches are commonly used in practice. In particular, in the first part we present an unsupervised approach that uses Kernel Spectral Clustering (KSC) on the sensor data coming from a vertical form seal and fill (VFFS) machine, in order to distinguish between normal operating condition and abnormal situations. Basically, we describe how KSC is able to detect in advance the need of maintenance actions in the analysed machine, due the degradation of the sealing jaws. In the second part we illustrate a nonlinear auto-regressive (NAR) model, thus a supervised learning technique, in the LS-SVM framework. We show that we succeed in modelling appropriately the degradation process affecting the machine, and we are capable to accurately predict the evolution of dirt accumulation in the sealing jaws.