Machine Failure

Data Pipeline

The ML models provide accurate predictions that help in avoiding downtime and improving productivity by learning from historical data collected from assets. Data sources we have used to validate this use case are – failure history, maintenance history, machine conditions and usage, machine features, and operator features.

Training Pipeline

Using Veltris Insight, we built predictive models that can identify equipment likely to need servicing and/or are more prone to failure. The framework helps to partition records into training, validation, and test sets to minimize the number of time intervals shared between them. ML models are then trained on older records and validated and tested using newer records to obtain an accurate assessment of the model’s performance.

Inference Pipeline

The framework provides for sampling techniques such as oversampling of the minority examples along with more sophisticated techniques to avoid false alarms. Faulty equipment, thereby identified, can proactively be removed and recalibrated, leading to significant improvements in manufacturing quality.

Monitoring And Logging

Veltris Insight also provides for dashboards to track efficiency, create predictive maintenance plans, and remotely monitor the real-time status of all equipment.

Machine Failure

Transform Maintenenace & Service Operations

Stay Up and Running

Data Pipeline

Training Pipeline

Inference Pipeline

Monitoring And Logging