How machine learning enables scalable Predictive Maintenance

Machine learning uses algorithms to find patterns in data to predict future events and make decisions faster without requiring manual analysis. Financial organizations have long used machine learning to successfully identify fraud and assist with investment decisions. Today, machine learning is available to many more sectors and departments offering opportunities for greater corporate efficiency and insights.

Being entirely data driven and quick to set up with relatively few people required, it brings insights and the benefits of scale to manufacturing operations that may otherwise have been priced out by hardware requirements and the recruitment, retention and training of highly skilled technicians and analysts.

Machine learning in a corporate world

Within a traditional manufacturing plant, machine learning can be used to turn on the smart switch to deliver corporate objectives such as continuous production or just in time production without having to invest in a data science team or complex technology platforms requiring long timeframes to implement without any success guaranteed.

What are the benefits of smart factories powered by machine learning?

Machine learning can be utilized by factories on any key operating asset to monitor in real time and in an automatic manner their condition. This real time analysis enables the team on the shop to not only focus their time on where they are most needed but also to predict when a machine will fail and intervene beforehand, avoiding unnecessary downtime. This smart approach which is known as Predictive Maintenance provides companies with a competitive edge.

Benefits of scalable Predictive Maintenance using machine learning

Skills & Time Management

Using machine learning in a smart factory means that engineers on the shop floor can get the data they need to prevent a failure from happening. Previously manual condition monitoring included the collecting of key indicators like temperature and pressure, but this can now be done and analysed automatically. Not only does it save time but it also allows engineers to fully focus their skills on where they matter the most: the maintenance plan and assets that need their attention.

Effectiveness

Combining real-time data with historical trends and variables such as current environmental factors, machine learning can make decisions on when action needs to be taken and make suggestions of what that action should be. The production team can then take appropriate action without requiring an understanding of algorithms and data trending.

Impact

This use of computing power is an example of technology being accessible to all organizations, large or small. With minimal staff resource required to set up or for ongoing monitoring, and the flexibility to scale as required, the only thing standing in the way of achieving impact in this way is an organization’s ambition to move away from traditional manual operations.

Requirements

Getting ready for this move requires a check of the organization’s setup for asset data gathering information and key condition indicators.

Getting the organization ready to become a smart factory also requires a shift in the corporate culture usually driven by the leadership or executive team. The employees need to understand and embrace the opportunities that new approaches like Predictive Maintenance powered by machine learning can bring.

Knowledge is power

Bringing machine learning to a factory unlocks the potential of its assets and its key condition indicators. Machine learning doesn’t have a maximum number of assets to monitor, it is able to manage as little or as much data put into it. And it gets more accurate with its insights as time goes by, as the additional data enriches its modelling and analysis experience. Providing insights, rather than straightforward monitoring of thresholds, the plant engineers  can focus where their time is needed the most and be more efficient with unplanned downtime reduction and machinery life extension. The tremors of this knowledge-based approach to site management are felt in all corners of an organization, as the impact empowers and challenges staff to identify and embrace new opportunities.

 

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