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AI for Predictive Maintenance Applications in Manufacturing

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • AI
  • Sustainable Growth and Tech Trends
  • Emerging Technologies


Publication | Update: Sep 2020

Predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment).

Taking advantage of artificial intelligence capabilities, such as of recognizing temperature, vibration, and other factors from sensors pre-built into machinery and vehicles, business leaders adopt predictive and preventative maintenance applications.

According to Pamela Bump, Managing Editor at Emerj, while predictive maintenance allows manufacturers to attempt to predict how long a piece of machinery will last, preventative maintenance involves repairing the machinery to keep it working longer.

With the digitization of many manufacturing plants and the reported costs of reactive maintenance needed to fix machinery in mind, Emerj has put together a list of five companies that claim to offer software using AI for predictive maintenance.

These companies offer software for use in two applications:

  • Asset Performance: The company’s primary goal is using sensors or other data to determine and notify manufacturers when a piece of machinery is broken or not working.
  • CMMS Systems: Systems that both monitor asset performance and allow users to assign and track repair work orders.

According to Daniel Faggella, Head of Research at Emerj, upkeep on physical equipment is expensive and time-consuming. In addition to the cost of human diagnostic efforts by trained professionals, equipment downtime can have downstream productivity impacts.

When a piece of manufacturing equipment (such as a drill or a lathe) is shut down for maintenance, the in-process inventory being fed to that machine must halt or be routed elsewhere. Backed up in-process inventory can create unpredictable downstream effects for other equipment, for transportation, and for the labor forces operating them – not to mention a potential failure to meet customer delivery expectations and deadlines.

Because the expense and impact of a breakdown or malfunction of equipment is so significant, manufacturing firms business must have consistent regiments of maintenance and upkeep in order to limit productivity disruption while also keeping a close pulse on the functioning of it’s most important equipment.

The potential for artificial intelligence to reduce downtime while also improving overall maintenance and monitoring of equipment is significant. Reducing risk and improving throughput (revenue) are important to any manufacturing operation.

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According to ReadITQuik review, businesses in the manufacturing space are looking at ways to optimize processes, both in terms of efficiency and costs. The use of data-backed “intelligent” algorithms is a great way to make that happen. As a result, businesses are turning to AI applications, one of the most beneficial ones being predictive maintenance.

Predictive maintenance is thus the core of manufacturing innovation and involves rethinking and optimizing the entire maintenance strategy as a whole from top to bottom.

Here is how companies must go about embracing this new technique:  

1. Understand the Need: The first step in moving toward predictive maintenance is to understand pain points (namely drivers of costs, waste, or inefficiency) and identify the best use case for your business.

2. Get Data: Of course, the proliferation of IoT plays a large role in predictive maintenance, especially with cheap sensors and data storage combined with more powerful data processing that has made the technology accessible. But, there are other data sources out there, which might include data from programmable controllers, manufacturing execution systems, building management systems, manual data from human inspection, static data such as manufacturer service recommendations for each asset, external data from APIs, like weather, that could impact equipment conditions or wear, geographical data, equipment usage history data, and parts composition.

3.. Explore and Clean Data: After identifying relevant data sets, it’s time to dig in. Ensure you really understand all the data you’re dealing with and that you know what all of the variables mean, what is being measured, and where all the data is coming from.

4. Enrich Data: Manipulating data at this stage means adding more features and connecting them in meaningful ways so that each data set, or data from multiple sensors, can be taken as a whole instead of in parts.

5. Get Predictive: It is this combination of a variety of sources and data types that allows for the most robust and accurate predictive models. The more sources and types of data available, the better the complete picture of a particular asset and the better the prediction.

6. Visualization: Visualization is an important tool in predictive maintenance as it often closes the feedback loop, allowing maintenance managers and staff to see the outputs of predictive models and direct their attention accordingly. Robust data science or data team tools today allow maintenance managers and staff on-the-ground to easily access and digest outputs in a familiar format so that the entire team, from analysts to technicians receive the same feedback.

7. Deploy: Deploying a predictive maintenance model into production means working with real-time data, but to iterate and deploy means providing visual real-time dashboards for on-the-ground maintenance teams. For some use cases, feedback can be integrated directly into the predictive maintenance process, requiring no or little human interaction.

Overall, predictive maintenance shows huge promise for the future with its potential cost savings, new revenue channels, and a high degree of automation leading to less dependence on human resources.

Read More on 5 Use Cases: https://emerj.com/ai-sector-overviews/ai-for-predictive-maintenance-applications-in-industry-examining-5-use-cases/

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Forecasts, Data modelling and indicator normalisation

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The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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