Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

• The concurrent pattern can be decomposed effectively and speedily using Haar wavelet.
• Support vector machines recognizer works well for recognizing concurrent patterns.
• SVM approach outperforms several neural networks for recognizing concurrent pattern.
• The proposed hybrid kernel has better capability than GRBF and polynomial have.
• Particle swarm optimization can find optimal parameter of kernel function.

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.