The Fifth Elephant 2023 Monsoon Edition event recap is now up here . The event was attended by 192 participants, of which one-fourth were women. The Fifth Elephant videos are available to watch here
Event highlights:
Editors
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
Tracks and themes
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
Members-only conference
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Pick a membership to attend the in-person conference. If you have questions about participation, post a comment here.
Who will benefit from participating in The Fifth Elephant community:
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Contact information
Join the @fifthel Telegram group or follow @fifthel on Twitter. For any inquiries, call Hasgeek at +91 7676 33 2020.
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SB
SHRINATH BHAT
@shrinathda
Reliability Analysis of Machine Tools Using Machine Learning
Submitted Jun 29, 2023
ABSTRACT
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
Introduction
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.
Problem
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.
Implication
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.
Solution
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.
Outline
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:
- Field failure exploratory data analysis to understand the failure pattern.
- Role of machine learning and artificial intelligence in the reliability engineering
- Study and exploration of reliability distributions and obtain the best-fitting distributions, including assembly and sub-system-level analysis of failure data.
- Optimization and selection of champion distribution models by using good fit statistics both analytically and graphically.
- Kaplan-Meier estimate of reliability and Nelson-Aalen estimate of reliability for better accuracy in the prediction of failures in machine systems.
- Duane reliability growth analysis.
- Optimization of the frequency of preventive maintenance by obtaining optimal replacement time and minimizing the overall maintenance cost in the process.
- Analysis of Trend in the Rate of Occurrence of Failures.
References
[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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