A Kohonen SOM based, machine health monitoring system which enables diagnosis of faults not seen in the training set

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.

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SME 4.0: Machine learning framework for real-time machine health monitoring system

Journal of Physics Conference Series ◽

10.1088/1742-6596/1911/1/012026 ◽

2021 ◽

Vol 1911 (1) ◽

pp. 012026

Author(s):

K Velmurugan ◽

P Venkumar ◽

Pandian R Sudhakara

Keyword(s):

Machine Learning ◽

Real Time ◽

Monitoring System ◽

Health Monitoring ◽

Time Machine ◽

Health Monitoring System ◽

Learning Framework ◽

Machine Health Monitoring ◽

Machine Health

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Conceptualization and Design of a Low-Cost MTConnect-Enabled Refractometer for Coolant Health Monitoring

Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing ◽

10.1115/msec2019-2755 ◽

2019 ◽

Author(s):

Thomas Feldhausen ◽

Asimm Hirani ◽

Walter King ◽

Roby Lynn ◽

Thomas Kurfess

Keyword(s):

Refractive Index ◽

Monitoring System ◽

Health Monitoring ◽

Network Architecture ◽

Low Cost ◽

Health Monitoring System ◽

Manufacturing Enterprises ◽

Scada System ◽

Machine Health ◽

Health Monitor

Abstract Monitoring of the health of water-based coolant used for machining requires measurement of various parameters of the coolant, including refractive index, temperature, pH, and turbidity. One of the primary parameters that is used to determine the concentration of the coolant is the refractive index, which is typically measured manually by an operator at regular intervals during machine operation. This paper describes the conceptualization and preliminary design of a coolant health monitoring system that will automatically measure the refractive index of the coolant and will digitize the resulting measurement for communication to a factory supervisory control and data acquisition (SCADA) system. To enable rapid integration into a factory’s network architecture, the coolant concentration measurement will be transmitted by the monitoring system using the MTConnect format. Having an MTConnect-enabled sensor will allow the data to be remotely aggregated and compared to other machine data to help give a better understanding of overall machine health. The economical approach to its design allows the coolant health monitor to be realizable for both small manufacturing enterprises (SMEs) and large manufacturers alike. This widespread implementation will further benefit industry’s movement toward Internet-of-Things (IoT)-equipped manufacturing facilities.

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Neural-Network-Based System for Novel Fault Detection in Rotating Machinery

Journal of Vibration and Control ◽

10.1177/1077546304043543 ◽

2004 ◽

Vol 10 (8) ◽

pp. 1137-1150 ◽

Cited By ~ 23

Author(s):

V. Crupi ◽

E. Guglielmino ◽

G. Milazzo

Keyword(s):

Neural Networks ◽

Health Monitoring ◽

Rotating Machinery ◽

Training Data ◽

Training Set ◽

Data Set ◽

The Past ◽

Machine Health Monitoring ◽

Machine Health ◽

Vibration Signatures

The purpose of this research is the realization of a method for machine health monitoring. The rotating machinery of the Refinery of Milazzo (Italy) was analyzed. A new procedure, incorporating neural networks, was designed and realized to evaluate the vibration signatures and recognize the fault presence. Neural networks have replaced the traditional expert systems, used in the past for the fault diagnosis, because they are a dynamic system and thus adaptable to continuously variable data. The disadvantage of common neural networks is that they need to be trained by real examples of different fault typologies. The innovative aspect of the new procedure is that it allows us to diagnose faults, which are not considered in the training set. This ability was demonstrated by our analysis; the net was able to detect the presence of imbalance and bearing wear, even if these typologies of faults were not present in the training data set.

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Quantification of Gear Tooth Damage by Optimal Tracking of Vibration Signatures

International Journal of Rotating Machinery ◽

10.1155/s1023621x97000146 ◽

1997 ◽

Vol 3 (3) ◽

pp. 143-151 ◽

Cited By ~ 2

Author(s):

F. K. Choy ◽

R. J. Veillette ◽

V. Polyshchuk ◽

M. J. Braun ◽

R. C. Hendricks

Keyword(s):

Monitoring System ◽

Health Monitoring ◽

Gear Tooth ◽

Operating Conditions ◽

Diagnostic Process ◽

Data Set ◽

Health Monitoring System ◽

Gear Teeth ◽

Gear Mesh ◽

Machine Health Monitoring

This paper presents a technique for quantifying the wear or damage of gear teeth in a transmission system. The procedure developed in this study can be applied as a part of either an onboard machine health-monitoring system or a health diagnostic system used during regular maintenance. As the developed methodology is based on analysis of gearbox vibration under normal operating conditions, no shutdown or special modification of operating parameters is required during the diagnostic process.The process of quantifying the wear or damage of gear teeth requires a set of measured vibration data and a model of the gear mesh dynamics. An optimization problem is formulated to determine the profile of a time-varying mesh stiffness parameter for which the model output approximates the measured data. The resulting stiffness profile is then related to the level of gear tooth wear or damage.The procedure was applied to a data set generated artificially and to another obtained experimentally from a spiral bevel gear test rig. The results demonstrate the utility of the procedure as part of an overall health-monitoring system.

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