A Survey of Data Mining Techniques for Quality Improvement in Process Industries

Statistical process controls (SPC) such as process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional SPC tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of complex and correlated data found in a variety of modern systems. As the limitations of SPC methodology have become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. Despite the great potential of data mining algorithms for addressing the challenging problems in SPC, few efforts have been made to integrate data mining algorithms with SPC. In this study we present how data mining algorithms can be used to effectively deal with massive amounts of complex data generated by modern complex systems.