DSAAS (DATA SCIENCE AS A SERVICE): STEPPING UP THE GEAR WITH VEHICLE PREDICTIVE MAINTENANCE
Picture this. You’re driving back home after a long day of hard work. Exhausted from a series of back-to-back calls, starving despite that quick, half-eaten sandwich you rushed through between one virtual meeting and another. Dying to finally get well-deserved rest.
But just then, a sharp squeal comes from under the hood. Is it an issue with brakes or a loose or worn fan belt? Maybe the steering system went down?
Either way, your dreams of a good evening’s rest are now ruined, and you’re up for towing or an unplanned inspection. Both of which are going to cost you dearly.
Now, what if all of that time and hassle could have been avoided?
Mobility predictive maintenance — the way to move forward
We won’t keep you in suspense. It could, and predictive maintenance is the way to achieve it. And not only on an individual driver’s level, but also when it comes to maintaining entire fleets of vehicles in good shape and preventing unexpected failures.
Vehicle predictive maintenance definition
Predictive maintenance generally refers to the combined powers of IoT sensors, data analytics, and artificial intelligence (AI) applied to forecast equipment failures before they occur and recommend repairs in time.
In the specific context of fleet management and vehicle maintenance, mobility predictive maintenance software uses:
- GPS readings,
- the Controller Area Network (CAN) bus data (the information from the electronic communications bus that gets different car parts “talk” to one another),
- vehicle maintenance records,
- and advanced AI predictive algorithms,
to come up with recommendations on impending failures or suggested repairs.
Why time-based maintenance belongs to the past
Coming back to our traumatic road event, situations like this are common when the traditional approach to vehicle maintenance is applied. By traditional, we mean maintenance based on mileage or, worse — estimated mileage.
Since individual driving style heavily impacts a car’s tear and wear, it is extremely challenging to accurately predict the lifetime of car parts using this method, which considers the same, generic metric, regardless of how someone drives. As a result, many issues occur that impact individual drivers, courier and transport services, car rental and sharing operators, and fleet managers. These include:
- Reduced car lifespan. Unexpected or repeated failures of vehicle components naturally shorten the car’s lifespan, resulting in lower residual value, higher costs, and greater waste.
- Unnecessary maintenance. For fleets, every hour of vehicle downtime translates into a loss. This is especially true when a car is fully operational or only needs a minor fix when it goes out of service for a day or more because of the time-based scheduled maintenance.
- Higher costs. The above factors combined generate higher spending on the car, its parts, and repairs. From unplanned downtime, through inefficient use of car parts, to higher insurance rates, relying on traditional maintenance schedules can incur significant (and unnecessary) costs, which could have been avoided by applying a modern predictive approach.
- Greater road hazards. Time-based maintenance also has another major flaw — it leaves room for failure between fixed inspections. Since no two drivers (or automobiles) are alike, the impact of their driving style on component wear can be extremely different. The implications of these differences may, at times, lead to tragic consequences.
Benefits of predictive maintenance for fleets and drivers
As opposed to traditional preventive maintenance techniques, predictive maintenance software considers numerous factors to infer the time to the next maintenance and accurately assess vehicle wear.
Instead of focusing solely on time estimates and the number of miles, it factors in the vehicle, driver, and contextual information (weather conditions, traversed routes, road conditions, past events, etc.). The result is reliable, fact-based insights into the actual vehicle wear and the factors that affect it. As a result:
- Maintenance and repairs can be carried out as required by the vehicle’s exact condition or its components.
- Constant monitoring of parts’ conditions helps minimize unanticipated failures and their impact and cost.
- This, in turn, directly reduces vehicle downtime and the occurrence of vehicle recalls.
- Conducting maintenance repairs only when required means limiting over-maintenance and no-defect-found cases.
- Moreover, reliable predictive maintenance solutions for car fleets are fully scalable, offering insights into the health of a single car, or an entire fleet, no matter how big.
In addition to these “standard” benefits, vehicle predictive maintenance solutions offer an unlimited number and variety of use cases they serve. Their sophistication depends only on one’s imagination and the data sets available.
For example, a car manufacturer or a fleet rental company may leverage the insights from predictive analytics to build a customer-facing app showing the current condition of a vehicle. Or by integrating car maintenance data into the systems, road assistance companies can better prioritize interventions and provide support faster and more efficiently.
How will I know when my brake pads need changing?
Here’s another way to look at predictive vs. preventive vehicle maintenance. Let’s take brake pads as an example. Traditionally, the owner or driver wouldn’t find out that they need changing until taking the vehicle to the inspection (or, worst-case scenario, when the brakes stop working properly en route). Meanwhile, predictive maintenance would allow them to learn exactly when they should schedule a part replacement based on the following key data:
- records of maintenance and repairs events, with an indication of the type of maintenance (service, breakdown), their timing (on time, too late, too early), effort or cost;
- reported fleet downtimes with details about the cause of breakdown;
- Diagnostic Trouble Codes (DTCs) transmitted via telematics devices of the car;
- an extra bit of contextual information, including km driven in specific road and weather conditions;
- in some cases, also an individual’s driving style and routines.
Taking all of these factors together, the predictive maintenance software would apply AI algorithms to offer precise information about which parts need immediate attention. Depending on the needs, how that information is passed on to the owner and through what interface can be fully tailored.
For example, the status of brake pads can be visualized as a simple graph that provides the percentage value (where 100% means a part is brand new, and the closer to zero, the higher the wear and shorter the lifespan). The information presented in this way is clear, efficient, and easy to read, but other visualization methods can also be used for the user’s convenience.
Which industries benefit from vehicle predictive maintenance?
Even though we’ve mainly discussed predictive maintenance in the context of an individual car, many sectors can take advantage of the technology’s capabilities. The specific benefits may slightly differ from one vertical to another (and if you want more details on that, get in touch with us regarding your needs).
Still, on the whole, they boil down to several key gains: proper maintenance, optimized car and parts lifetime, lower downtimes, safer trips, and lower costs.
Here are a few main beneficiaries:
Key takeaway: Tying the maintenance schedule solely to the driven miles is inefficient and outdated. Keeping vehicles in optimum operating condition relies on so much more than just the distance covered. Continued advancements in IoT, telematics, AI, and data science technologies have enabled a much more accurate and cost-efficient approach.
Let’s rewind to the moment you’re leaving the office and about to get in your car. With predictive maintenance systems under your hood, you can rest assured that your road runs smoothly, and you’ll get back home just in time for dinner. Safe, sound, and with savings in your pocket.