Predictive Maintenance Technology: Benefits and Applications

In the past, determining when to perform maintenance on a machine relied on historical data and scheduling routine upkeep. However, this approach had limitations.

Predictive maintenance examines specific data points relative to a connected machine or asset, such as:

• A meter
• A laser cutting machine
• A front-end loader

This technology takes those data combinations and determines when to initiate maintenance.

Different Levels: Threshold Analysis and Statistical Analysis

There are levels to predictive maintenance, like threshold analysis and statistical process analysis, which include pattern recognition. Threshold analysis is a simplified anomaly detection method. It will trigger an alert if conditions (e.g., vibration or temperature) exceed a set limit.

Statistical process analysis leverages complex multivariate AI engines to examine time series data. As predictive maintenance technologies advance to better sift through data subtleties, they move into statistical analysis. Here, AI and deep learning attempt to discover pertinent data trends.

An ideal industrial Internet of Things (IIoT) platform will support predictive maintenance levels for various use cases and deployments.

What Are the Benefits of Predictive Maintenance Solutions?

Previous maintenance models relied on scheduled maintenance, causing factories to halt a perfectly functioning machine for repairs. However, this cautious strategy isn’t cost-effective and hampers equipment uptime.

On the other hand, if scheduled maintenance is too late, an unplanned equipment failure could result in prolonged downtime. Picture an assembly line at an automobile factory where a machine breaks before its scheduled maintenance. The two hours the machine is down could mean millions of dollars lost as technicians scramble to fix the issue.

With predictive maintenance, factories can perform maintenance at the right time. Knowing when to take a machine offline without affecting overall productivity is invaluable. It boosts revenue and optimizes resource management.

Predictive Maintenance Applications

In addition to maximizing equipment productivity and minimizing downtime, many companies will use predictive maintenance for life cycle management. From a connected machine perspective, front-end loader companies can collect data concerning the status of parts within their vehicles. This data includes fault codes and other data points relevant to the health of key machinery.

Some applications may involve catching errors with assets or parts rather than equipment and machines. Using statistical analysis tools, like trained AI or an inference engine, automotive and large equipment manufacturers can take a picture.

For example, a wheel on an assembly line can be analyzed for scratches or missing lug nuts. Plus, predictive maintenance data can help these organizations make current and future maintenance planning decisions and gain valuable process insights.

Enable Preventive Maintenance Technologies with Telit Cinterion

Once a predictive maintenance system has made an inference, the most vital step is notifying personnel. Still, without enterprise integration with business tools or applications, predictive maintenance technology can’t automate a trouble ticket.

deviceWISE® platform, powered by Telit Cinterion, supports enterprise integration with predictive maintenance through low-code and no-code tools. It lets you connect your machines and enterprise software without requiring developer-level programming.

In addition, advanced predictive maintenance applications are leveraging AI and machine learning (ML) with the deviceWISE AI applications.

Speak with our experts to discover the incredible features of deviceWISE.