Cetaris uses cutting-edge Predictive AI and Advanced Reporting technologies to transform fleet and fixed asset maintenance. By integrating artificial intelligence (AI) into its analytics framework, Cetaris empowers users to predict failures, plan interventions, and improve performance across the board. The journey from data collection to actionable insights is reshaping maintenance strategies while driving cost efficiency and uptime.
The Roadmap to Advanced Analytics
An Industrial Internet of Things (IIoT) roadmap exemplifies the evolution of maintenance analytics.
Enabling IIoT and all that accompanying data helps with the transition from simple prediction to prescriptive decision-making and action. At the heart of this approach is machine learning (ML), which enables accurate forecasts and informed decisions. Key stages in the roadmap include:
- Collecting and Training Data
- Gather comprehensive datasets, including historical failure records and real-time performance metrics.
- Use these datasets to train ML models capable of predicting future asset failures based on specific conditions.
- Continuous Data Collection
- Continuously gather new data to refine the model’s accuracy and adaptability.
- Predicting Failures
- Utilize ML models to generate predictions with confidence levels, such as identifying an 85% likelihood of failure within two weeks.
- Acting on Insights
- Surface predicted failure cases, allowing maintenance teams to proactively plan interventions, reducing unplanned downtime.
The Data Science Lifecycle in Action
The Data Science Lifecycle represents the stages a data science project typically goes through, from its inception to its deployment. The exact number and terminology of the phases can vary based on different methodologies, but a common lifecycle comprises the following stages:
- Business Understanding
- Define objectives, such as improving asset reliability or reducing maintenance costs, in collaboration with stakeholders.
- Data Collection
- Source diverse data types, from part failure logs to tire tread depth inspections, enabling robust Remaining Useful Life (RUL) forecasting.
- Data Preparation
- Clean and preprocess the data to ensure accuracy, removing outliers and normalizing datasets for analysis.
- Data Exploration
- Perform statistical analysis and visualization to uncover patterns and relationships critical to maintenance strategies.
- Feature Engineering and Selection
- Refine the dataset by creating meaningful features and selecting variables that maximize model performance.
- Modeling
- Train predictive models using advanced ML algorithms, employing techniques like cross-validation and hyperparameter tuning for optimization.
- Evaluation
- Test models against key metrics (e.g., Mean Absolute Error for regression) to ensure reliable predictions.
- Deployment
- Integrate the trained model into production systems, enabling real-time insights and automation.
- Monitoring and Maintenance
- Regularly assess model performance and update as needed, ensuring alignment with evolving data trends.
- Feedback Loop for Continuous Improvement
- The lifecycle includes a feedback loop, where insights from monitoring guide refinements in the process. This iterative approach ensures long-term relevance and accuracy of the predictive AI system.
From Insight to Action
If that all sounds a little complicated, it doesn’t have to be. Cetaris simplifies predictive AI and data science into highly usable analytics tools that can be used to get ahead of challenges and gain increased control over maintenance operations. The transition from prediction to prescriptive action streamlines decision-making, reduces operational costs, and enhances asset reliability.
Book a demo to speak to our sales team about how our reporting tools can be tailored to your unique organizational needs.