Deep learning for predictive maintenance : Towards Industry 4.0
Why Industry 4.0 matters?
Just 13 % of organizations have attained the complete effect in their digital investments, so empowering them is in demand to have financial upside and make digital expansion. The optimal combination of analytics/deep learning with IoT can save large and SME’s around $16 billion.
What’s predictive maintenance (PdM) of Industrial physical assets?
This is a online-monitoring system which requires hardware and software components, including condition monitoring sensors, gateways and modules to handle data processing and transmission, and a secured cloud server to handle data storage and data analytics.
Why is this important to Industries?
Cost, safety, availability, and reliability are the main reasons why key industrial players are investing in predictive maintenance. Predictive maintenance allows factories to monitor the condition of in-service equipment by measuring key parameters like vibration, temperature, pressure, and current. Such monitoring requires connected smart sensors featuring a high-speed signal chain, powerful processing, and wired and/or wireless connectivity.
Considering the above sections, as in the case of any machine learning implementations, there are hidden and underlying challenges involved in implementing PdM for industries.
To tackle this, our research group has come up with focused solution to seamlessly integrate machine learning algorithms and industrial IoT platform. The real challenge is twofold. Apart from the technical trials, this is more of a need for agreement among plant engineers and research community.
- To bring awareness among engineers about industry 4.0
- To have technically sound way of implementing PdM
- Providing deliverables and have ROI
Keywords: Predictive maintenance, Industry 4.0, Behavioral change
Outline/Structure of the Talk
- What is the need for Industry 4.0?
- Organizational challenges
- Technical issues and ways to solve the problem of predictive maintenance by deep learning
- Demonstration of practical use cases in renewable energy and manufacturing sector
- Why to integrate simulation with deep learning !
- What can be the deliverables ?
- To understand the importance of physical asset management
- The different types of challenges
- Technical ways to reach deliverables
- Understanding the practical use cases
- How to implement this in other firms and scale up !
Data Engineers, Data Scientists
schedule Submitted 4 years ago
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