3 Steps for Moving Toward a Predictive Maintenance Model

Predictive maintenance offers great promise for precision machining shops, but not all shops have the resources for immediate implementation. Here are some small measures to start making smart and informed decisions on maintenance strategies and scheduling.


Facebook Share Icon LinkedIn Share Icon Twitter Share Icon Share by EMail icon Print Icon

Machine learning revolutionizes industries across the board by helping teams analyze mountains of data to find patterns, anomalies and actionable insights. Manufacturing and precision machining are not left out of this equation, especially when it comes to the potential of predictive maintenance.

At its heart, predictive maintenance uses self-learning algorithms driven by data and explicitly trained to perform a particular task. Predictive models require near real-time data from sensors on machines in the modern shop or factory. Based on the data, the self-learning algorithms help human operators monitor the quality of the resulting machined parts and characteristics of the machines. This helps better predict when to perform maintenance, ultimately saving the organization time, money and resources.

While predictive maintenance offers great promise for precision machining shops, it’s not an immediately accessible strategy for all organizations due to upgrade costs and a technology skills gap. But teams can still take incremental steps to start harnessing the benefits of predictive maintenance.

Most of the industry already operates on a preventive rather than reactive maintenance schedule. While that’s a positive step toward preempting machine breakdowns and bolstering efficiency, preventive maintenance is not a perfect solution. Often, it can lead to teams servicing parts prematurely, yielding wasted hours and costs for maintenance that didn’t need to be performed yet.

Machines can’t tell us how they’re feeling—but data can. That’s why predictive maintenance holds particular promise. If we’re able to glean real-time insights from machines, such as temperature, pressure, speed, vibration or images of certain parts, we can better understand the load on a machine and streamline its maintenance.

In addition to saved time, materials and labor, it creates a safer environment for machine operators. The machine learning is better able to predict when machines will fail, keeping operators out of harm’s way.

Predictive maintenance via machine learning will one day be commonplace in precision manufacturing. But as of now, many organizations can’t commit to quick and dedicated implementation within their own operations due to technology, skills and budget limitations.

For most teams, implementation would be a decade-long process. It requires integrating physical on-premises equipment with cloud-based technology—something that takes niche expertise and plenty of budget. However, small steps can be made toward a smarter maintenance model. Like any major digital transformation initiative, it is best done mindfully and incrementally.

  1. Snag the low-hanging fruit. A shop can start by capturing one or two specific data points that are relatively easy to access. From there, they can analyze the resulting data to see what it can teach them about the preventive maintenance process. Creating a proof of concept is a great way to offer experimentation and serve as an example for the organization’s leaders to prove the value of predictive maintenance. Mainstream cloud platforms such as Amazon AWS and Microsoft Azure offer information and capabilities for getting started with machine learning and predictive analytics, which form the basis of predictive maintenance.
  1. Research add-ons. There are many niche offerings in the market capable of enhancing legacy equipment without requiring a major overhaul. These technologies generally add sensors that allow near real-time data to be extracted from machines, and then securely push the data to the cloud to analyze patterns, anomalies and other actionable insights. These modifications are a common approach for manufacturing teams to begin adapting their on-premise, SCADA-based (supervisory control and data acquisition) infrastructure. This will begin the integration with cloud-based machine learning and predictive maintenance models while taking a sensible, incremental approach.
  1. Make sure IIoT is on your road map. The Industrial Internet of Things (IIoT) will transform the future of the manufacturing industry. In the coming years, every aspect of the manufacturing process will be interconnected, leading to completely optimized production and maintenance planning and operations. To stay on track with competitors and customer expectations, organizations must make sure their digital transformation plans for IIoT are in their formative stages now. This involves ensuring the company’s C-suite is aware of your team’s needs for such a transformation, including a timeline, hiring plan to acquire the right talent, purchasing needs and budget requirements. Even if it’s not an approved plan, it is critical to make leaders aware of how the landscape for manufacturing and precision machining will change in the future.

Predictive maintenance holds plenty of promise for the precision machining and manufacturing industry. While not all organizations are in a position to immediately take advantage of such a strategy, it doesn’t mean they can’t take productive steps toward full use of machine learning in place of their current preventive maintenance schedule. Ultimately, the most important step is to have predictive maintenance on the roadmap for your organization.


About the Author

Matt Mead

Matthew Mead

Matthew Mead is the chief technology officer at SPR. He has more than 20 years of experience designing and delivering complex, mission-critical technology solutions using a variety of technologies. By leveraging industry best practices, mobility, open source initiatives and an agile approach, Matthew works to reduce the cost and risk for large software development initiatives.