Data Mining in Manufacturing & Process Industries

The use and application of Neural Networks (NN) has found a “home” in the domain of industrial process control. At the same time, NN is practically a core function in most popular data mining solutions. NN algorithms have been embedded in process control solutions, yet sometimes seen or even projected as a bit of a “black box” or “magic box”. Obviously, because of the complexity involved for most process control engineers to rationalize the output of an NN algorithm.

Root Cause Analysis (RCA) has traditionally been conducted by core statistical applications in order to identify cause of failure of plant equipment. RCA is classified based on the use or objectives as:

1. Safety-based RCA, which descends from the fields of accident analysis and occupational safety and health
2. Production-based RCA, which has its origins in the field of quality control for industrial manufacturing.
3. Process-based RCA, which is an “add-on” to production-based RCA, but with a scope that has been expanded to include business processes.
4. Failure-based RCA is rooted in the practice of failure analysis as employed in engineering and maintenance.
5. Systems-based RCA emerged as an amalgamation of the preceding uses, along with ideas taken from fields such as change management, risk management, and systems analysis.