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Reliability Analysis of Machine Tools Using Machine Learning

Submitted Jun 29, 2023


Submitted by
Shrinath Bhat
Mechanical Engineering Dept., IIT Madras 2020 graduate
Senior Data Scientist, BEES Algo Selling team, AB InBev

Keywords: Reliability engineering; Reliability growth analysis; Goodness of fit statistics; Preventive maintenance; Optimal replacement time; Rate of Occurrence of Failure

Machine tools are highly important for the manufacturing industry. Machine tools are used for various applications like; glass working, parts reclamation, metal spinning, metalworking, woodturning, etc., and their reliability has an important significance in the processing quality and production efficiency.

Machine tool failures restrict the production efficiency of industries and lead to high economic losses. Large industrial facilities lose more than a day’s worth of production each month and hundreds of millions of dollars a year to machine failures, according to a new report published in June 2023 by Senseye, the AI-powered machine health management company.

There are various implications associated with machine failures, including downtime and production loss, increased maintenance, and repair costs, reduced efficiency, and productivity, financial loss and revenue impact, etc.

Reliability analysis of machine tools is the key to reducing downtime of customers’ machines and the manufacturers’ service cost. As defects in machines cannot be eliminated, proactive maintenance is vital. Proactive maintenance especially is favorable to the industry as a solution to minimize machine downtime and the repairing cost.

Determining the subsystem’s reliability and evaluating the whole machine’s reliability is important for predicting the lifespan, allowing us to manage its lifecycle. Machine Learning and Artificial Intelligence play an important role in reliability engineering. The Duane reliability growth analysis is done for failure data of repairable machine systems, which will help in further machine improvement by eliminating design deficiencies. Optimization of the frequency of Preventive maintenance leads us to Optimal replacement time and minimum overall cost of maintenance. Analysis of trends in machine systems’ Rate of Occurrence of Failures supports the reliability growth objective. Reliability growth analysis followed by simulation of the actual usage is done and failures are identified each time. Failure analysis and rectification in the design are performed to reduce future failures with the same causes.
The following objectives shall be covered in the talk session:

  1. Field failure exploratory data analysis to understand the failure pattern.
  2. Role of machine learning and artificial intelligence in the reliability engineering
  3. Study and exploration of reliability distributions and obtain the best-fitting distributions, including assembly and sub-system-level analysis of failure data.
  4. Optimization and selection of champion distribution models by using good fit statistics both analytically and graphically.
  5. Kaplan-Meier estimate of reliability and Nelson-Aalen estimate of reliability for better accuracy in the prediction of failures in machine systems.
  6. Duane reliability growth analysis.
  7. Optimization of the frequency of preventive maintenance by obtaining optimal replacement time and minimizing the overall maintenance cost in the process.
  8. Analysis of Trend in the Rate of Occurrence of Failures.

[1] Yongjun Liu, Hua Peng, and Yong Yang. (2018) Reliability Modeling and Evaluation Method of CNC Grinding Machine Tool.
[2] Balaban H. S. (1978) Reliability growth models.
[3] Reid, M. (2020) Reliability - A Python library for reliability engineering.


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