A large American Automobile Manufacturer wanted to use Diagnostic Trouble Codes for anomaly detection and indicator of potential part failure in future for its four-wheeler business to do preventive maintenance to save costs
10,000 DTC series codes were studies for pattern analysis to validate existing sequence of DTCs before failure. Parallel processing was done on SPARK SCALA, .9 mn vehicle data and 1.5 Petabyte of data analyzed by applying random forest algorithms
Independent Power Produces (IPP;’s) currently spend on costly LiDAR technology to measure the degrees of YAW MISALIGNMENT for WIND Turbines. High cost, delayed results and lack of scalability are some of the challenges faced.
We developed a digital lidar based on real time SCADA data applying machine learning techniques. Multiple models were developed to pick Random Forest as the best model. The client can now see real time yaw misalignment based on last 1 months.
Identifying defects in Oil & Gas pipelines running into hundreds of kilometers is a challenge for companies. The data captures through pipeline inspection gauge is huge and identifying dents, ovality and corrosion if of prime interest for oil and gas players.
Providing schedule of energy forecast is a mandatory compliance for IIP’s and they often face heavy penalty for going beyond the forecast. The client was facing a high penalty when they approached us with the problem.
Due to a competitive market in the renewable business its essential for the IPP’s to have self O&M capacity. The client has outsources O&M to OEM’s and it was impacting their bottom line.
Clients existing operating platform has solar monitoring and alarm alerting capacity. However unable to know the equipment fault and anomaly insights for its assets.