Monitoring the condition of parts and machine components is a crucial task in ensuring fault-free manufacturing. In this work, we propose an alternative condition monitoring technique, with great potential in extracting and isolating individual fault patterns from manufacturing signals. We propose that the Karhunen-Loeve transform provides the ability to decompose measured signals into decorrelated fault patterns, in the form of fundamental eigenvectors. These fundamental eigenvectors can then be monitored by means of coefficient vectors, which indicate any changes in the fault patterns. The technique can provide accurate fault information, whether the manufacturing signals are deterministic, stochastic, stationary, or nonstationary. This paper presents the fundamentals of the proposed technique and its extension to condition monitoring. The outputs of the Karhunen-Loeve transform are studied to interpret their physical significance. Then, a subset of general manufacturing signals is used to understand the mathematical foundations of the technique. Extensions to general functions are investigated by means of numerical simulations. The technique proposed in this paper has great potential in providing a robust condition monitoring tool.
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