The Internet of Things (IoT) sensors collect equipment operation data (such as temperature, vibration, current, etc.) in real time, and combine historical maintenance records and production data to build a multi-dimensional data set. For example, Schneider Electric's predictive maintenance system captures more than 20,000 data points per second through vibration sensors and uses edge computing gateways for real-time analysis. Data cleaning technologies (such as outlier detection and feature extraction) can remove noise and ensure the quality of model input.

Data Collection and Sales

Deep learning models (such as LSTM and CNN) can capture the time series characteristics of equipment operation and predict the remaining useful life (RUL). Reinforcement learning algorithms can dynamically adjust maintenance plans to balance preventive maintenance costs and downtime risks. Clustering algorithms (such as K-means) can identify time-wasting links in the production process.

Assessment framework construction

Drawing on the “lean agent” concept, the assessment is broken down into independent modules such as equipment health monitoring, maintenance strategy optimization, and cost analysis, which work together through standardized interfaces.