Manufacturers are increasingly turning to machine learning (ML) to solve a long-standing problem: preventing costly downtime. A recent case involving a U.S.-based manufacturer highlights how predictive maintenance systems powered by AI can revolutionize equipment management by detecting faults in real time and ensuring seamless operations.
Traditionally, manufacturers have relied on reactive and preventive maintenance strategies. Reactive maintenance addresses equipment failures after they occur, often resulting in significant downtime costs—sometimes reaching hundreds of thousands of dollars per hour. Preventive maintenance, while aiming to avoid breakdowns, involves regularly replacing parts, leading to increased maintenance costs and unnecessary waste.
The Shift Toward Predictive Maintenance
The advent of AI and ML is now transforming these outdated practices. With predictive maintenance, manufacturers analyze real-time data to identify potential equipment issues before they occur. This approach extends machine life, improves operational efficiency, and reduces waste. Importantly, it also mitigates safety risks by ensuring that failing components do not jeopardize other parts of the system or endanger workers.
The U.S.-based manufacturer in this project faced challenges managing a complex hydraulic system, where a single malfunction could disrupt the entire operation. To avoid unexpected breakdowns, the company aimed to build an ML model capable of predicting the wear and tear of key components, including coolers, valves, pumps, and hydraulic accumulators.
Turning Data into Actionable Insights
The company had already installed condition monitoring sensors and collected large datasets reflecting the degradation levels of individual components. However, transforming this raw data into a functioning AI system proved challenging. To achieve their goal of 95% predictive accuracy, the manufacturer turned to NineTwoThree, an AI engineering firm based in Boston.
The firm installed new physical and virtual sensors to expand data collection capabilities. This generated a vast amount of data, covering load cycles and measured parameters from the hydraulic system. However, the project quickly ran into challenges with data complexity—initially producing over 43,000 features that could negatively impact the model’s accuracy.
Reducing Complexity to Improve Accuracy
Managing the vast dataset required exploratory analysis to identify and remove redundant information. NineTwoThree’s data scientists discovered that many sensors were highly correlated, posing the risk of multicollinearity—a scenario where multiple independent variables are too closely related, compromising the model’s predictive performance.
After refining the dataset, the team successfully reduced the number of features from 43,680 to 136, making the data more manageable and easier to analyze.
Developing High-Accuracy ML Models
Once the data was streamlined, the next step involved building predictive models to forecast the condition of the key components. Four machine learning models were developed—one each for predicting the performance of the cooler, valve, pump, and hydraulic accumulator.
The results were impressive, with the models achieving accuracies exceeding 99%. These high-accuracy models allowed the manufacturer to detect component inefficiencies early and generate real-time alerts for the maintenance team. Each notification detailed the specific part requiring attention, along with the nature of the issue.
Smarter Maintenance, Greater Efficiency
By adopting AI-powered predictive maintenance, the manufacturer has not only reduced unplanned downtime but also minimized maintenance costs and extended the lifespan of their equipment. The system ensures that parts are only replaced when necessary, avoiding premature replacements and unnecessary expenses.
The benefits of predictive maintenance extend beyond cost savings. Real-time fault detection helps prevent small issues from escalating into larger problems, ensuring uninterrupted production and maintaining workplace safety.
A Growing Trend in Manufacturing
The success of this project underscores the growing importance of predictive maintenance across the manufacturing industry. Whether companies are highly data-mature or just beginning to explore AI solutions, the value of machine learning in maintenance is becoming clear.
Experts believe that AI-powered maintenance systems will play a critical role in the future of manufacturing, offering a competitive edge to companies willing to invest in predictive analytics and AI infrastructure.
“This case highlights how machine learning transforms maintenance strategies, offering not just efficiency but also safety and cost savings,” said a representative from NineTwoThree. “The ability to predict equipment failure with such precision is a game-changer for manufacturers aiming to optimize operations.”