Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.

[1]  Sofiane Achiche,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..

[2]  Yuqian Lu,et al.  Standards for Smart Manufacturing: A review , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[3]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .

[4]  Liu Yang,et al.  Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test , 2020, Int. J. Pattern Recognit. Artif. Intell..

[5]  M. Kulkarni,et al.  A review on diagnostic and prognostic approaches for gears , 2020, Structural Health Monitoring.

[6]  Ching-Hung Lee,et al.  Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function , 2020, Sensors.

[7]  Michele Monno,et al.  A review of prognostics and health management of machine tools , 2020, The International Journal of Advanced Manufacturing Technology.

[8]  Khosrow Dehnad,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[9]  Lincoln C. Wood,et al.  Big data analytics as an operational excellence approach to enhance sustainable supply chain performance , 2020 .

[10]  N. R. Sakthivel,et al.  Tool condition monitoring techniques in milling process — a review , 2020 .

[11]  Imtiaz Ahmed Choudhury,et al.  Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring , 2016 .

[12]  Mohsen Guizani,et al.  Internet of Things Architecture: Recent Advances, Taxonomy, Requirements, and Open Challenges , 2017, IEEE Wireless Communications.

[13]  Ahmet Özdemir,et al.  Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring , 2016, The International Journal of Advanced Manufacturing Technology.

[14]  Yen-Chi Chen,et al.  A tutorial on kernel density estimation and recent advances , 2017, 1704.03924.

[15]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[16]  Tommy W. S. Chow,et al.  Anomaly Detection and Fault Prognosis for Bearings , 2016, IEEE Transactions on Instrumentation and Measurement.

[17]  Fausto Pedro García Márquez,et al.  Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure , 2020, Renewable Energy.

[18]  L. N. López de Lacalle,et al.  Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals , 2008 .

[19]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[20]  G. B. Benitez,et al.  The expected contribution of Industry 4.0 technologies for industrial performance , 2018, International Journal of Production Economics.

[21]  Gamini P. Mendis,et al.  Monitoring of a machining process using kernel principal component analysis and kernel density estimation , 2019, Journal of Intelligent Manufacturing.

[22]  Alessandra Caggiano,et al.  Cloud-based manufacturing process monitoring for smart diagnosis services , 2018, Int. J. Comput. Integr. Manuf..

[23]  Fan Zhang,et al.  Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence , 2014, Journal of Mechanical Science and Technology.

[24]  Ahmed A. D. Sarhan,et al.  Measuring of positioning, circularity and static errors of a CNC Vertical Machining Centre for validating the machining accuracy , 2015 .

[25]  Yu Yang,et al.  Element analysis and wear longevity calculation of an O-ring in the actuator cylinder of a certain aircraft landing gear , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[26]  C. Hopkins,et al.  A Review of Developments in the Fields of the Design of Smart Cutting Tools, Wear Monitoring, and Sensor Innovation , 2019, IFAC-PapersOnLine.

[27]  H. A. Kishawy,et al.  Optimization Methodologies in Intelligent Machining Systems - A Review , 2019 .