The importance of fleet maintenance is often underestimated – it is the backbone of a company’s fleet operations. It’s the most effective way of reducing vehicles’ unexpected breakdowns and accidents caused by vehicle malfunctions. Many businesses have adopted the preventive fleet maintenance strategy aimed at lessening the likelihood of equipment failure. But what does the future hold for the fleet maintenance field? How do we further elevate the current maintenance strategy to a whole new level? The answers to these questions rely upon whether we can explore and activate the full potential of artificial intelligence (AI) and machine learning (ML) technology in the maintenance subject. Both of these technologies have gradually become a constant presence in a wide range of industries, and how well we can integrate them into the fleet maintenance field will be decisive to the future of fleet management.
What Is Predictive Maintenance?
With the power of AI and ML, what we can achieve is limitless. The intelligent forecasting tool powered by AI is a game-changing instrument that makes predictive maintenance possible. Predictive maintenance is a huge leap forward compared with preventive maintenance. It’s a much more sophisticated and technologically-advanced maintenance model that is more effective in helping businesses save time and money. Preventive maintenance only focuses on scheduling periodic maintenance based on average or expected lifecycles of an asset – whether or not the upkeep is actually needed. It did not take into account the actual health and status of the parts and components of the vehicle.
The predictive maintenance model raises the bar of maintenance management by collecting real-time asset operational data as well as studying both the current and historical vehicle data and operating patterns to suggest the best path ahead to fix the issues. The system will only intervene and recommend a repair when the issues are at the nascent stage, right before they can interrupt the everyday operations. This is a significant improvement over the preventive maintenance as it offers a more targeted shutdown approach resulting in shorter vehicle maintenance downtime and reducing unnecessary maintenance inspections.
Here is a quick comparison between the preventive maintenance model and the newly improved predictive maintenance strategy.
How It Works?
There is no doubt that predictive maintenance is far superior and advanced in almost every category of comparison. This model leverages the power of AI, ML, big data analytics, cloud data storage technologies and connects all the smart hardware pieces with the Internet of things (IoT) to structure and produce highly accurate forecast models that assist technicians in precisely identifying faulty units and quickly conducting repair work.
You might be wondering what kinds of metrics the predictive maintenance models look at to produce the forecast results. In fact, the system scans through almost every little detail associated with the asset, including vehicle model, make, year of production, warranty, vehicle uptime and downtime, historical maintenance records, reliability index, mileage and a whole lot more. This data is collected from various vehicles’ onboard sensors such as tire pressure monitoring systems, engine sensors and is transmitted through wireless networks to the cloud database.
Then, the computer applies an advanced machine learning model to study the data, frame the problem appropriately, and make predictions. The regression models to predict vehicles’ remaining useful lifetime and the classification models to predict a failure within a given time window are two of the common machine learning algorithms used by the system. The system will extract the necessary information and data needed, such as the historical failure events, mechanical properties, and typical usage behaviours, to predict when the next possible equipment failure may happen. The whole data collection and interpretation process is all happening in real-time during the operations, which could only be done by ML and AI technologies.
The Unrivalled Benefits of Predictive Maintenance Model
No more manual tweaking or calculation is needed, as ML and AI algorithms automate the entire process. Fleet managers no longer need to assign technicians to conduct routine inspections of all vehicles in the fleet since predictive maintenance systems have already conducted full diagnostics of the vehicles and sent all the reports and recommendations to the fleet managers. The system identifies potential service disruptions and recommends appropriate risk mitigation activities. It can suggest optimal maintenance schedules and automatically create and assign work orders to responsible technicians.
From a business perspective, implementing predictive maintenance strategies helps to keep the operational and maintenance costs low and effectively reduce vehicle downtime and service disruptions. An unexpected vehicle breakdown halfway during its operation can be a costly problem for any business. It means the vehicle has to be taken out of service, and companies have to send out a replacement vehicle to perform a rescue. The drivers face risks of exceeding the Hours of Service limit, forcing businesses to spend extra money to send out a new crew to complete the unfinished tasks. Customers might also face a delay in receiving shipments or services because of an unplanned vehicle breakdown, which negatively affects customer satisfaction. With the introduction of a predictive maintenance strategy, all these worries and unpleasant surprises will be eliminated. Fleet managers can place full confidence in their fleet for delivering the best performance and efficiency thanks to a sophisticated maintenance program
Integrating Predictive Maintenance Into CMMS
To fully experience the power of predictive maintenance, fleet managers need to integrate it with the Computerized Maintenance Management System (CMMS) to create a streamlined workflow in the maintenance department. The integration process can roughly be categorized into three steps.
Condition monitoring is about establishing the standard for different vehicle health metrics and using advanced sensing and testing technologies such as ultrasonic, infrared thermography (IR), and partial discharge testing to capture and extract the vehicle’s health status information. The system will then compare the collected data with the pre-set standard and automatically create a work order if collected data falls outside the acceptable range.
But measurement comparisons are not enough to showcase all the details of a vehicle. This is where AI and ML come into place to help analyze the data and make recommendations. All the data collected from vehicles’ onboard sensors will be wirelessly synced to the CMMS, where AI and ML will study and produce customized vehicle health reports to the maintenance technicians, so they can quickly get into action and fix the problem. There is no need to hire additional personnel to identify and analyze issues as the computer has done them all in high precision and instantaneous speed.
The data integration is the final step in the process, as the main focus is on creating a streamlined workflow between different departments in the corporations. There is nothing more important than simplifying technician’s workflow as the predictive maintenance model’s sole focus is about delivering the best possible experience for them. Technicians need to be able to access work orders and see each vehicle’s status and information anywhere they go. They need to be able to create an intuitive CMMS that allows the maintenance team to see all fault alerts and problem severity easily. There is an urgent need to simplify the work reporting procedures and improve information sharing within the corporation. The CMMS provides the management team and other corporate departments with the same clear view of all the maintenance progress, including any finished or unfinished tasks, so everyone in the corporation can stay on top of the entire maintenance operations.
The predictive maintenance model is the future standard of asset maintenance. It offers customers an unmatched level of automation and accuracy that traditional preventive maintenance and corrective maintenance models cannot provide. With continuous improvement and optimization in the AI and ML algorithms, the predictive maintenance model has a great potential to become even smarter and more precise.