Importance of AI in Predictive Maintenance
Data is involved in every industry. With the help of the more sophisticated AI algorithms, more insights are gathered from data to uncover what is unknown. Manufacturing, particularly automotive, is one of the industries which gets the most out of AI, especially in predictive analytics. In this writing, AI’s impact on predictive maintenance will be examined.
Regular maintenance actions are taken in the manufacturing industry to avoid unplanned downtimes. However, most of the time, operators cannot foresee the breakdowns before it occurs. Wide-predictive maintenance solutions are rule-based; therefore, they cannot always react to changing patterns. In addition, defining these rules is tough to implement and not easily scalable. Rule thresholds differ from one machine to another.
With the involvement of AI in the manufacturing sector, maintenance solutions also got more competent. Nowadays, AI-based predictive maintenance solutions can detect anomalies several hours or days before machine failure occurs. Thanks to more generalized algorithms, solutions are easily scalable to multiple machines. Moreover, the implementation is relatively straightforward. There is no need for additional hardware.
Simplicity is the key while generating Machine Learning models. Complex ML models are prone to memorizing data, and while testing in production, these models are more inclined to respond to every bit of inconsistencies during manufacturing. Complex models tend to trigger the alarm system that warns the operators even if there is no malfunction. Eventually, operators lose trust in the model’s capabilities. However, ML models can detect anomalies with a relatively simple model. It is essential to find the sweet spot between simplicity and complexity not to trigger the alarm often, but only when there is an actual defect. As the models become more generalized this way, they can also be quickly adapted to new machines.
In fast-paced industries, there is no time for a stoppage caused by unplanned downtime of the machines. In top manufacturers such as our client producing around 600 cars per day, the cost of halting the line for a couple of hours is just too high to risk. When it comes down to maintaining these machines, the action has to be proactive rather than reactive.
In KoçDigital, AI-based predictive maintenance models can be generated from a single electricity current data from IoT devices. With high-frequency raw electricity data, AI models can create insight to detect anomalies that leads to significant malfunctions in the machines. During the two-month pilot period at our auto-manufacturer client, our models detected 6 hours of unplanned downtime. Six hours of stopping time may considerably impact the manufacturing line in the automotive industry. Besides having a better functioning manufacturing line, well-planned maintenance orders help to reduce maintenance costs.
Current predictive maintenance methodologies in AI have a substantial effect on detecting anomalies. However, without the prescriptive part, models cannot understand the source of the problem. With the advancements in AI models, prescriptive analytics will be used more in manufacturing in the near future.
Berk Baris | Senior Analytics Consultant, KoçDigital
