Scientists combine equipment health monitoring, detection, and forecasting to keep systems operating.

Like any manufacturing equipment, semiconductor fabrication systems have a finite lifetime. Technicians normally perform maintenance on these hardware systems according to preset schedules and regardless of actual need, which results in unnecessary equipment downtime and needless costs incurred as a result of lost production time and additional maintenance labor. AFRL scientists teamed with researchers from the University of New Mexico (UNM) to examine the feasibility of establishing prognostics for such expensive and valuable machinery and to devise a mechanism for scheduling equipment maintenance based on needs rather than calendar cycles. This so-called condition-based maintenance has the potential to increase equipment availability, improve productivity, enhance safety, and reduce expenses. The ultimate objective of the AFRL/UNM collaboration is to develop a data-driven prognostic system that provides advanced warning of failures, faults, and other error events that occur in complex systems.

A functioning system, be it a machine tool or an aircraft, creates a dataset in n-dimensional space. This dataset contains certain recognizable characteristics, or signatures, with each signature denoting a particular system condition or event. Conversely, a malfunctioning system generates different datasets and creates different signatures; complex partitions in the n-dimensional dataset separate these signatures. Because a complex system can have a substantial number of unique conditions or events, the volume of generated information applicable to forecasting maintenance needs is well beyond the capacity of the engineer or machine operator to process. Scientists can detect the distinct signatures associated with the state of a system using methods such as bounds checking, statistical analysis, neural networks, fuzzy logic, data mining, classical expert systems, and hierarchical and hybrid systems. Each of these signature detection techniques is a good candidate for use in prognostic systems, and all are described in more detail in the following paragraphs. The AFRL/UNM scientific team chose to integrate various soft computing techniques with these established methods to discern the subtle clues contained in this massive network of information.

Operators routinely employ the original diagnostic technique—bounds checking—as an effective prognostic tool. In this approach, instruments monitor operating parameters, such as temperature, pressure, voltage, current, and load, to ensure they fall within predetermined operating limits established by experience and prior statistical studies. Additionally, operators can apply several well-known statistical analysis techniques to effectively compare the system’s current state to distinct feature vectors. Statistical analysis can also provide prognostic information when software routines employ it in conjunction with prior operational knowledge.