Predict Equipment Failures Before They Happen
AI algorithms analyze sensor data from your trucks to find patterns that precede failures. They learn from past repairs to predict when a specific part will need service.
Syntora offers expertise in developing custom AI-driven predictive maintenance solutions for industries like logistics and transportation. Our approach leverages sensor data and machine learning to anticipate equipment failures, enabling proactive maintenance scheduling for small fleets.
Building a predictive maintenance system requires access to historical telematics data and maintenance logs. Syntora's approach to complexity depends on data quality; a fleet with clean telematics data and digitized repair invoices allows for a more streamlined development process than one with inconsistent logs stored across various sources.
What Problem Does This Solve?
Most small fleets rely on the basic alerts from their telematics provider, like Samsara or Geotab. These systems are reactive. They generate a fault code when a component is already failing, giving you a 'Check Engine' light when the truck is 500 miles from your home terminal. They show you what is broken now, not what is about to break next week.
A fleet manager for a 20-truck operation tried tracking this in a spreadsheet. After every repair, he would note the mileage and component. This manual process is impossible to scale and cannot uncover complex patterns. He could see that a specific truck model needed new brakes every 80,000 miles, but he could not see that a gradual 5% decrease in oil pressure over 90 days was a leading indicator of an impending turbo failure across multiple trucks.
This reactive approach leads to expensive emergency repairs and delayed deliveries. A single unscheduled roadside repair can cost over $2,000 and disrupt schedules for days. The standard telematics dashboards provide historical graphs, but they do not connect the dots between subtle sensor changes and future expensive failures. They provide data, not foresight.
How Would Syntora Approach This?
Syntora's approach to predictive maintenance for small fleets would begin with an in-depth discovery phase to understand your specific operational needs and data landscape. We would connect to your telematics provider's API to pull historical sensor data, ensuring efficient data retrieval using Python scripts with the httpx library for asynchronous requests. This raw data would then be cleaned, structured, and stored in a robust database solution like Supabase Postgres, integrated with your digitized maintenance logs.
From this consolidated data, Syntora would engineer a comprehensive set of features to capture operational trends and anomalies. Leveraging libraries like pandas, we would calculate metrics such as rolling averages, standard deviations, and rates of change for critical sensor readings like coolant temperature and exhaust backpressure. These engineered features would serve as the input for a machine learning model, typically a gradient boosting model built with LightGBM, which would be trained to predict the probability of specific component failures within a defined prediction window tailored to your operational cycle.
The developed model would be packaged in a Docker container for consistency and deployed as a serverless function, often utilizing AWS Lambda, accessible via a lightweight FastAPI endpoint. A scheduled job would routinely trigger this function, pulling the latest sensor data, executing predictions, and updating a health score for each truck in your fleet. This automated process ensures continuous monitoring and timely insights.
When a truck's failure probability for a monitored component crosses a pre-defined threshold, the system would be configured to trigger immediate alerts. These alerts could be delivered to a Slack channel or directly via email to your fleet manager. Each alert would provide the truck identifier, the component at risk, the probability score, and key sensor readings that influenced the prediction, enabling proactive maintenance scheduling and reducing unscheduled downtime.
What Are the Key Benefits?
Get Your First Prediction in 4 Weeks
From data connection to live predictions in 20 business days. Schedule proactive maintenance based on model outputs, not just mileage.
Avoid Expensive Roadside Repairs
Proactively address issues in your own shop during scheduled downtime. Stop paying premium rates for emergency third-party service.
You Own the Model and the Code
You receive the complete source code in a private GitHub repository. There are no black boxes or vendor lock-in.
Alerts Before the Check Engine Light
The system detects subtle patterns that precede fault codes. Get notified of a potential failure weeks before the dashboard lights up.
Connects Directly to Your Telematics
We pull data directly from your existing Samsara, Geotab, or other telematics provider API. No new hardware needs to be installed on your trucks.
What Does the Process Look Like?
Week 1: Data Connection and Audit
You provide API credentials for your telematics system and export 12+ months of maintenance logs. We deliver a data quality report outlining the predictable failure types.
Weeks 2-3: Model Development
We build and train the predictive models on your historical data. You receive a validation report showing the model's accuracy on past, known failures.
Week 4: Deployment and Integration
We deploy the system on AWS and configure the alerting workflow. Your team receives the first live predictions for your active fleet.
Post-Launch: Monitoring and Handoff
We monitor model performance and alert thresholds for 90 days. You receive the full GitHub repository and a runbook detailing system operation.
Frequently Asked Questions
- What does a predictive maintenance system cost?
- Pricing is based on the scope of the project. Key factors include the number of trucks in your fleet, the number of data sources, and the quality of your historical maintenance logs. A fleet with clean, structured data will require less setup time than one with handwritten logs that need to be digitized. We provide a fixed-cost proposal after the initial discovery call at cal.com/syntora/discover.
- What happens if a prediction is wrong?
- The model provides a probability, not a guarantee. There will be false positives and missed events. We work with you during the monitoring phase to tune the alert sensitivity to match your operational tolerance. The goal is a significant net reduction in unscheduled downtime. The system also tracks its own accuracy, allowing for continuous improvement as more data is collected.
- How is this different from the analytics in my Samsara dashboard?
- Your Samsara dashboard provides excellent historical reporting and real-time fault code alerts. It tells you what happened or what is happening now. We use that same data to build a forward-looking model that predicts what is likely to happen in the next 2-4 weeks. It is the difference between a smoke detector and a fire risk assessment.
- What specific types of failures can you predict?
- The system learns from your fleet's unique repair history. Common predictable failures tied to sensor data include issues with DPF systems, alternators, water pumps, turbos, and brakes. The model cannot predict random events that have no leading sensor indicators, such as a tire blowout from road debris or a cracked windshield. We identify the most likely predictable failures during the initial data audit.
- Do we need an IT team to run this after it is built?
- No. The system is built on serverless AWS Lambda functions, which means there are no servers for you to manage or patch. It runs automatically. We provide a 90-day monitoring period, and after that we offer an optional, flat-rate monthly plan to handle ongoing monitoring, retraining, and support. Most clients do not require an internal technical team.
- How much historical data is needed to get started?
- We need a minimum of 12 months of telematics data and corresponding repair logs. The logs must specify which truck was repaired, on what date, and which component failed. For statistical significance, we need at least 20-30 recorded instances of a specific failure type to build a reliable model for it. The more clean historical data you have, the more accurate the initial model will be.
Ready to Automate Your Logistics & Supply Chain Operations?
Book a call to discuss how we can implement ai automation for your logistics & supply chain business.
Book a Call