A This approach helps induste downtime and d 'agriante costs by enabling timely interventions.

A projekt felülvizsgálata

Ez a goál was to create a system capable of analizing sensor data from machinery to pressent potential failures. Ez a projekt involved data collection, model training, deployment, and ongoing monitoring to ensure precinaciy and reliability.

Data Collection és d Preparation

A Sensor data was gyűjt from variouk machines overa a Persod of six months. Te data included temperature, vibration, pressure, and operationad l hour. Data cleaning contingved removing anomalies and filling missingg valores to previle e for analysis.

Model Development

Machine learningg models such as Random Forest and Support Vector Machines were trend on historical data. Features were werereed to enhance predikte power. The models were validated using cross validation technokes to overfitting.

A program végrehajtása

A best- performing model was integrated into the producturing system via an API. It provided edied real-time prediktions, alerting provide teams of potential failures. Te implementation resulted in a 20% reduction in in un unplanned downete and lower properante ces.