Comparing Machine Learning and Statistical Process Control for Predicting Manufacturing Performance

2019 ◽  
pp. 108-119
Author(s):  
Sibusiso C. Khoza ◽  
Jacomine Grobler
Keyword(s):  
Machine Learning ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Manufacturing Performance

scholarly journals IMPLEMENTATION OF STATISTICAL PROCESS CONTROL (SPC) FOR MANUFACTURING PERFORMANCE IMPROVEMENT

1970 ◽  
Vol 40 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Farzana Sultana ◽  
Nahid Islam Razive ◽  
Abdullahil Azeem
Keyword(s):  
Quality Management ◽  
Process Control ◽  
Total Quality Management ◽  
Statistical Process Control ◽  
Data System ◽  
Total Quality ◽  
Statistical Process ◽  
Machine Breakdowns ◽  
Hourly Data ◽  
Manufacturing Performance

This paper intends to combine the Hourly Data System (HDS) and Statistical Process Control (SPC) practices to improve manufacturing performances in manufacturing companies. The focus of this work is to find out the frequencies and time duration of machine breakdowns as well as the major causes of breakdowns affecting productivity. Total quality management (TQM) was introduced to improve continually the products or services to increase the customer satisfaction level. SPC is an important tool of TQM. Again HDS is the real time view of production floor of any manufacturing industry. In usual practice, SPC is used as quality control tool. However in this research SPC is used to increase total output identifying major loss times from various machine breakdowns using HDS. Successful implementation of the recommendations of this paper can significantly improve the manufacturing performance of a manufacturing environment. Keywords: Total Quality management (TQM), Statistical Process Control (SPC), Hourly Data System (HDS)   doi: 10.3329/jme.v40i1.3466 Journal of Mechanical Engineering, Vol. ME40, No. 1, June 2009 15-21

scholarly journals Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 23427-23439 ◽  
Author(s):  
Jyh-Yih Hsu ◽  
Yi-Fu Wang ◽  
Kuan-Cheng Lin ◽  
Mu-Yen Chen ◽  
Jenneille Hwai-Yuan Hsu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Process Control ◽  
Wind Turbine ◽  
Statistical Process Control ◽  
Predictive Maintenance ◽  
Statistical Process

scholarly journals End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1484
Author(s):  
Chuen-Sheng Cheng ◽  
Ying Ho ◽  
Tzu-Cheng Chiu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Process Control ◽  
Convolutional Neural Network ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process ◽  
Random Patterns ◽  
Control Chart Pattern

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.

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