Predictive Emissions Control System Maintenance Automation

This workflow automates the continuous monitoring of diesel particulate filters (DPF), selective catalytic reduction (SCR) systems, and exhaust sensors by ingesting real-time telemetry from engine control units (ECUs). It predicts filter clogging, catalyst degradation, and sensor drift, triggering automated regeneration cycles or scheduling cleaning services before efficiency drops or regulatory thresholds are breached. The operational upside comes from avoiding costly non-compliance penalties, maintaining optimal fuel consumption, and preventing unplanned downtime due to emissions-related derates or shutdowns.

Implementation integrates directly with asset telematics platforms and CMMS systems like SAP or Maximo. The architecture requires robust validation rules to prevent false positives, human-in-the-loop approval gates for high-cost interventions, and automated documentation for audit trails. Rollout must sequence by machine type and account for variable data quality from older assets. The result is a closed-loop system that protects project margins by ensuring fleet availability and compliance without manual monitoring overhead.

This workflow automates the continuous monitoring of emissions-critical subsystems, a manual and reactive process prone to oversight. By ingesting real-time data from engine control units (ECUs), NOx sensors, and backpressure readings, it predicts DPF clogging and SCR catalyst degradation before they trigger fault codes or compliance violations. The operational upside is direct: avoiding non-compliance penalties, maintaining optimal fuel efficiency, and preventing costly unplanned downtime for forced regenerations or component replacement.

Implementation requires a layered architecture with edge processing for low-latency alerts and cloud-based agents for complex prognosis. The Diagnostic Agent uses retrieval-augmented generation (RAG) to pull OEM service bulletins and historical repair data, while the Action Logic agent integrates with CMMS like Maximo for work orders and ERP for parts. Critical controls include human approval gates for non-standard interventions, automated audit trails for compliance evidence, and staged rollouts to validate model accuracy against physical emissions tests before full fleet deployment.

Phase 1 establishes the core data pipeline and single-asset proof of concept. We integrate directly with engine ECUs via J1939/CAN bus or OEM telematics APIs to ingest real-time data on DPF backpressure, NOx sensor readings, exhaust gas temperatures, and regeneration cycles. This data is streamed to a cloud-based orchestrator (e.g., built on LangGraph) where initial anomaly detection models flag deviations from baseline performance. The goal is to validate the data quality, model accuracy, and the basic alert-to-work order flow in your CMMS (e.g., SAP PM, Maximo) for one machine, proving the technical and business case before scaling.

Phase 2 expands to a pilot fleet of 10-20 high-value assets, integrating the orchestrator with your CMMS for automated work order generation and parts forecasting. Here, we implement human-in-the-loop approval gates for high-cost interventions and establish exception routing for sensor faults. Phase 3 is the full fleet rollout, adding a governance layer for monitoring model drift, tracking compliance reporting to environmental systems, and optimizing the workflow based on realized reductions in fuel waste, non-compliance risk, and unplanned downtime. Each phase delivers measurable operational leverage while de-risking the overall architecture.

Effective governance starts with data quality controls at the edge. Telemetry from engine control units (ECUs) and exhaust sensors must be validated for accuracy before ingestion. A rules engine classifies anomalies—such as a rising DPF backpressure trend—and triggers an initial diagnostic agent. This agent retrieves historical maintenance logs from the CMMS (e.g., SAP PM) and contextual data like fuel quality to assess failure probability. All data flows and agent decisions are logged to an immutable audit trail, essential for environmental compliance reporting and validating maintenance actions against regulatory standards.

Phased rollout mitigates implementation risk. Phase 1 pilots the workflow on a single asset class, like a fleet of excavators, integrating only with the primary CMMS. Success is measured by reduction in unplanned SCR-related downtime and avoidance of emissions non-compliance fines. Phase 2 expands to all diesel assets and integrates with environmental reporting systems for automated documentation. Phase 3 introduces full closed-loop control, where authorized agents can initiate parked regenerations automatically. Each phase includes defined rollback procedures and requires a governance committee sign-off before proceeding, ensuring the system scales without compromising operational safety or regulatory standing.