The status of fault patterns on part surfaces can provide valuable information about the condition of a manufacturing system. Accurate detection of the part surface condition in manufacturing ensures the fault-free manufacturing of high-quality parts, as well as helping in the accurate design/redesign of machine components and manufacturing parameters. To address this problem, we introduce an alternative mathematical transform that has the potential to detect faults in manufacturing machines by decomposing signals into individual components. Specifically, the paper focuses on the decomposition of numerically generated data using the Karhunen-Loe`ve transform to study a variety of signals from manufacturing. The potential utility of the proposed technique is then discussed in the context of understanding a manufacturing process under constant development. [S1087-1357(00)01801-3]
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