Using Text Visualization to Aid the Analysis of Machine Maintenance Logs

Quality control becomes an important key for companies in suppressing the number of defective produced products. Six Sigma is a quality control method that aims to minimize defective products to the lowest point or achieve operational performance with a sigma value of 6 with only yielding 3.4 defective products of 1 million product. Stages of Six Sigma method starts from the DMAIC (Define, Measure, Analyze, Improve and Control) stages that help the company in improving quality and continuous improvement. Based on the results of research on baby clothes products, data in March 2018 the percentage of defective products produced reached 1.4% exceeding 1% tolerance limit, with a Sigma value of 4.14 meaning a possible defect product of 4033.39 opportunities per million products. In the pareto diagram there were 5 types of CTQ (Critical to Quality) such as oblique obras, blobor screen printing, there is a fabric / head cloth code on the final product, hollow fabric / thin fabric fiber, and dirty cloth. The factors caused quality problems such as Manpower, Materials, Environtment, and Machine. Suggestion for consideration of company improvement was continuous improvement on every existing quality problem like in Manpower factor namely improving comprehension, awareness of employees in producing quality product and improve employee's accuracy, Strength Quality Control and give break time. Materials by making the method of cutting the fabric head, the Machine by scheduling machine maintenance and the provision of needle containers at each employees desk sewing and better environtment by installing exhaust fan and renovating the production room.

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Machine operator dies after being struck by a shredder grate during shredding machine maintenance.

10.26616/nioshsface06mi009 ◽

2007 ◽

Keyword(s):

Machine Maintenance ◽

Machine Operator

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Text Visualization Analysis of Middle School Students' Discourse According to Writing Performance Levele

Korean Association For Learner-Centered Curriculum And Instruction ◽

10.22251/jlcci.2017.17.15.401 ◽

2017 ◽

Vol 17 (15) ◽

pp. 401-422

Author(s):

SeulKi Lee ◽

◽

YoungMin Park ◽

Keyword(s):

Middle School ◽

Middle School Students ◽

School Students ◽

Writing Performance ◽

Text Visualization ◽

Visualization Analysis

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Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽

10.3390/act10020027 ◽

2021 ◽

Vol 10 (2) ◽

pp. 27

Author(s):

Sehrish Malik ◽

DoHyeun Kim

Keyword(s):

Smart Factory ◽

Prediction Algorithm ◽

Utilization Rate ◽

Task Management ◽

Management Mechanism ◽

Machine Maintenance ◽

Machine Utilization ◽

And Performance ◽

Artificial Neural Network Ann ◽

Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

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Analyzing Massive Machine Maintenance Data in a Computing Cloud

IEEE Transactions on Parallel and Distributed Systems ◽

10.1109/tpds.2011.306 ◽

2012 ◽

Vol 23 (10) ◽

pp. 1831-1843 ◽

Cited By ~ 58

Author(s):

Arshdeep Bahga ◽

Vijay K. Madisetti

Keyword(s):

Machine Maintenance

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Assessment of driving quality and its impact on changes in the technical condition of self-driving cars

Sel'skohozjajstvennaja tehnika: obsluzhivanie i remont (Agricultural Machinery: Service and Repair) ◽

10.33920/sel-10-2003-03 ◽

2020 ◽

pp. 24-26

Keyword(s):

Artificial Intelligence ◽

Life Cycle ◽

Computer Technology ◽

Technical Condition ◽

Entire Life ◽

Entire Life Cycle ◽

Machine Maintenance ◽

Current Task ◽

Self Driving Cars

The article describes the current task of developing and improving existing technologies for machine maintenance throughout the entire life cycle. The use of modern achievements in the field of computer technology, digitization of information, as well as the development of artificial intelligence technologies, will allow you to get new scientific and engineering results aimed at managing the technical condition of machines in operation.

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