PdM Module

• Reduction in Breakdown events
• Reduction in Downtime hours
• Reduction in Maintenance Costs
• Failure Prediction
• Better inventory management
• Predictable product quality
• Overall equipment effectiveness
• Faster delivery to market

Input(s):

• Data involving diagnostic and performance data, maintenance histories, failure data, operator logs and design data

Main feature(s):

• Anomaly detection remaining useful lifetime estimation probability of failure of a machine/component

Output(s):

• Robust failure and anomaly detection to prevent failures and unplanned downtime in multi-industry domains
• A rich set of ML models such as LSTM Networks, Variational Autoencoders, Isolation Forests, Deep Convolutional Networks etc. to predict failures and anomalies
• An ML service API that allows the users to start/stop various ML processes based on their current requirements
• An AutoML interface to make the users understand and observe the dynamics

• Manufacturing companies may not have enough data
• Even if they have data, they may not have accompanying failure or anomaly data
• Manufacturing companies may not have enough skill set in AI and ML

Manufacturers with different levels of need.

Licensing