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Impact of predictive analytics on DPF maintenance

Dec. 20, 2023
By pairing predictive analytics with various DPF regeneration modes, fleets can anticipate and respond to aftertreatment issues before they’re left with a derated vehicle.

In the pursuit of operational efficiency and reduced downtime, forward-thinking fleets are increasingly turning to the power of data and predictive analytics. One crucial component fleets monitor is the same that most say is the biggest source of issues on the road: the diesel particulate filter (DPF).

In a recent Pitstop survey taken by 200+ fleet professionals, 51% pointed to the DPF or diesel exhaust fluid as the main causes of breakdowns in their fleets. But leveraging predictive analytics can help fleets better understand and anticipate issues related to DPFs, such as soot buildup. With this technology, fleets can not only streamline maintenance processes but also enhance overall performance and adhere to environmental regulations.

DPFs and predictive maintenance

Diesel vehicles equipped with aftertreatment systems face challenges related to the DPF, which captures soot emitted by the engine. To prevent clogging, the system must undergo periodic regeneration to burn off the soot.   When soot levels exceed predefined thresholds, vehicles enter a state of “derate.” That leads to reduced power output, compromised performance, and increased fuel consumption. Beyond these operational challenges, derates can result in longer routes, diminished productivity, elevated maintenance costs, and potential breakdowns.

Read more: Wrongfully condemned: How to give DPFs a clean record

The key to mitigating these challenges lies in predicting and preventing derate events through continuous monitoring of aftertreatment sensors. By focusing on parameters such as DEF fluid level, DPF soot level, and regeneration status, fleets can proactively manage DPF maintenance. This approach allows for early detection of issues, providing fleet managers and technicians with a strategic window of opportunity for intervention before derate events occur.

DPF regeneration modes

In order to achieve predictive maintenance instead of reactive maintenance, it is crucial to understand the intricacies of DPF regeneration modes. These modes include passive regeneration, active regeneration, parked regeneration, and forced regeneration. By leveraging data from these modes, aftertreatment systems can track and predict the need for regeneration events, offering valuable insight into the overall condition of the system and forming the basis of aftertreatment system tracking.

With aftertreatment sensors, we can track active regenerations and predict if and when one will be necessary. We can also monitor the success of completed active regenerations and incomplete active regenerations, which signal a worsening state of the DPF. Additionally, active regeneration tracking data forecasts the need for parked regeneration and its potential for success.

Finally, combining aftertreatment sensors, active and parked regeneration tracking, and fault code information lets fleet managers predict the likelihood of a vehicle derate and the ensuing forced regeneration.

Proactive alerts and intervention

To avoid derates and forced regens, fleets can use predictive analytics and timely alerts so that both managers and technicians can anticipate potential derate events. This advanced warning system empowers proactive maintenance measures within a 24- to 72-hour window, minimizing the impact of derates on operational efficiency. Fleet stakeholders can also address issues before they escalate, resulting in improved efficiency, reduced breakdowns, and better control over operational costs.

Beyond addressing DPF concerns, this strategy can apply to several vehicle components throughout the entire vehicle life cycle. By adopting a holistic perspective, fleets can enhance overall performance and foster efficiency, safety, and profitability.

The integration of predictive analytics into fleet maintenance represents a paradigm shift in addressing real-world challenges in the transportation industry. By focusing on the predictive maintenance of DPFs, fleets can navigate this evolving landscape and utilize these strategies throughout their vehicle for increased uptime. 

About the Author

Jessica Kim

Jessica is the Head of Marketing and Revenue Operations at Pitstop, an AI-powered predictive maintenance software that harnesses available vehicle data to curb rising maintenance expenses and enable data-driven decisions to improve overall operation efficiency. Through a combination of growth marketing, business development, and innovative go-to-market strategies, Jessica has played a key role in redefining the way our industry understands fleet management, contributing to Pitstop’s leading voice and brand within predictive analytics.