Unveiling the top 5 secrets to a successful predictive maintenance solution

“We were able to correlate our prediction with the OEM’s brake pad measurements and it did seem to do quite a bit better than their predictions.”

Predictive maintenance is a nuanced yet powerful tool fleets are already using as part of their maintenance program. Just ask Vedant Khattar, Pitstop’s Chief Technology Officer, and Christopher Mah, Pitstop’s Chief Science Officer, who appeared in a Fireside Chat with Pitstop. They shared insights from more than five years worth of experience building and implementing predictive models for Pitstop’s customers.

They chatted about how predictive maintenance simplifies decision-making with data-driven recommendations. They also explored the requirements to create a “truly” predictive maintenance solution. Finally, they touched on how Pitstop takes a thoughtful approach to helping fleet managers overcome data overload with its features.

1. Maintenance Optimization

Predictive maintenance solutions should not just focus on identifying equipment failures. They should also help optimize maintenance schedules to reduce downtime and maintenance costs. By optimizing maintenance schedules, organizations can reduce maintenance costs while improving equipment reliability and uptime.

“Pitstop is a cloud platform first and foremost. Functionally speaking, it’s a suite of tools that will help fleet managers and others do predictive maintenance. What that means is we’re trying to predict problems before they happen. We’re trying to give reasonable notice so people can avoid unplanned breakdowns because those are particularly expensive. Of course, breakdowns are always going to happen, but if you know they’re going to happen, then you can schedule a shop visit,” says Christopher Mah.

One of the benefits of predictive maintenance is that it can help save time and money by reducing unnecessary trips to the repair shop or service center. 

“That’s quite an advantage. For example, let’s say shop time is scarce; you want to manage that and avoid having a whole bunch of breakdowns happening unexpectedly.” Additionally, by providing fleet managers with timely information about upcoming maintenance needs, they can plan ahead and take preventative measures before larger issues arise. This, in turn, leads to better vehicle performance and cost savings due to fewer breakdowns and repairs.

2. Data Quality

It is not enough to simply have data; it needs to be reliable, accurate, and up-to-date in order for the models to be effective. You need to make sure you have access to all relevant data points across all vehicles in your fleet—including information about parts, service history, driver behavior, etc.—in order to accurately predict when maintenance will be needed.

“We do get service records from many of our customers, and so we’re able to correlate our predictions of problems with what actually appears in the service records. It’s also possible to correlate some of the trouble codes with our algorithms,” Christopher Mah points out.

“We worked with a major manufacturer on the brake model, and our brake model is based on vehicle speed which is a fairly indirect measure of braking. But, by tabulating it over time, we’re able to get a fairly accurate measure of brake wear. The manufacturer had a bunch of technicians actually making measurements of brake pads, and we were able to correlate our prediction with the OEM’s brake pad measurements, and it did seem to do quite a bit better than their predictions.” (*hint hint* 94%+ accurate)

3. Advanced Analytics

Predictive maintenance is not just about collecting data. It’s about analyzing that data to identify patterns and predict failures before they occur. Advanced analytics, such as machine learning algorithms, artificial intelligence, and statistical analysis, are crucial for identifying trends and patterns in data that can lead to asset failure.

“We’re trying to predict failures and help people, and we have a lot of data to work with. Some of it is copious and useful, and some of it is not so useful. We have to sort through the pile of data to figure out what’s useful. Then see what patterns will make helpful predictions for us so we can look through components that look like they’re on their last legs,” Mah explains.

“You can look at components (i.e., brakes, tires, battery, etc.) that might be wearing out. We can look at data from components operating in unusual ranges of high temperatures. Then we know something’s wrong. Those are  the strings we have to pull, and then we design algorithms around that.”

4. Continuous Monitoring of Assets

Monitoring equipment continuously is critical for detecting early warning signs of equipment failure. Continuous monitoring ensures that any abnormalities or changes in equipment behavior are detected early, allowing maintenance teams to take corrective action before a failure occurs.

“One of the main features that we have that has been extremely useful for our customer base has been the fault code management system that we’ve built. These fleets have thousands of fault codes being generated based on the fleet size. It’s creating a lot of noise. So, they’re looking for certain fault codes that can’t be ignored, certain fault codes that are extremely important, and some that are important but can wait until the next service visit,” Vedant Khattar explains. 

“What we build on our side is a way to manage these fault codes and build classification algorithms that will classify if a fault code is a critical fault code, a major fault code, or a minor fault code. What that does is it really helps the fleet manager filter out the noise and tell them, hey, these are the severe fault codes that you as a fleet manager should be worried about today or in the upcoming week.”

“That’s been one of our most popular features, and it also does various other exciting things, such as clearing the fault codes on its own, so you don’t have to manually go and mark those as completed. It looks at engine hours so let’s say if that fault code came up and then after five hours of engine or driving time, that fault code didn’t appear again, it’ll mark itself as complete by itself.”

5. Ongoing Improvement

Predictive maintenance is an ongoing process, and it is essential to continually evaluate the program’s performance and make improvements. Regularly reviewing data and performance metrics can help identify areas for improvement and ensure that the program remains effective over time.

“Work order information is one piece that we leverage and distinguishes us from other platforms. We combine the knowledge of work order information and what happened to that vehicle in the real world. Then we combine that knowledge with the telematics data. The sensors that are giving us all the algorithm insights are then tied up with the work order data so we can see how accurate our algorithms are,” Khattar says.

“We’re constantly evolving our algorithms and asking what are the main failures these fleets are having and what are some of those failures that we can predict ahead of time. We do a lot of analysis on that, and how we built our ingestion system is fairly simple. Users can go on the platform, and they can upload CSV files of the work order data so we can easily upload that data into our system. Or they can share those files through emails. Sometimes we work with fleets and help them create a process where we can take their service history data and upload it to our backend directly.”

Discover how Pitstop’s predictive maintenance can transform your fleet

In summary, there are several requirements necessary for creating an effective predictive maintenance solution—data accuracy, transparency, and scalability among them—and Pitstop takes a thoughtful approach toward helping its customers achieve these goals. With its suite of features designed specifically for fleets like yours (including real-time insights into vehicle health), Pitstop can help you get ahead of any potential problems before they become significant issues down the line. If you’re looking for an efficient way to manage your fleet’s maintenance needs effectively with minimal effort on your part, then look no further than Pitstop!

Contact our team or book a demo to learn more about how Pitstop’s predictive maintenance can forecast high probable failures from multiple data sources, to improve efficiency and costs.