A curve interpretation and diagnostic technique for industrial processes

Detecting manufacturing problems as soon as they occur is important for efficient manufacturing in today´s factories. Many of these problems could be minimized by installing diagnostic systems to monitor manufacturing steps. A diagnostic technique has been developed to analyze process parameters and observables that change over time. Process parameters control the operation of equipment, and observables are attributes of a partially completed product. The technique uses a specified digital signal processing algorithm known as dynamic time warping (DTW) to transform the input signal into symbolic data. Knowledge-based diagnosis is performed on the symbolic data to determine malfunctions. A detailed description of the DTW algorithm and knowledge-based analysis is presented. Two different applications-one in the glass industry and another one in the semiconductor industry-are discussed to illustrate the general use of this technique