1. General Equipment Model

Mainly targets fault models at the component level of machinery and pump equipment (such as bearings, gears, etc.), using single-channel signals for analysis. The main processing steps are loading configuration tables - data reading - feature extraction and preprocessing - algorithm modeling - outputting conclusions to the application layer.

2. Multi-Sensor Fusion Model

Mainly for equipment diagnosis of large units such as compressors and internal combustion engines. The model input data consists of multi-channel sensor data, fusing multiple sensor data for intelligent fault diagnosis decision-making. The main processing steps are loading configuration tables - multi-channel data reading - feature value matrix - algorithm modeling - outputting conclusions to the application layer.

3. Multi-Model List

Research and training of 113 fault diagnosis algorithm models have been completed and can be directly deployed (no on-site learning and training required). The algorithm models cover dynamic and static electrical instrument equipment in industries such as building materials, petrochemicals, metallurgy, non-ferrous metals, energy, coal, home appliances, and automobiles. The official evaluation accuracy of the fault diagnosis algorithm software is 96%, the average accuracy reported by customers is 75%, and the accuracy combined with manual diagnosis reported by customers is 98%.

4. Advantage - Generalized Algorithm

Does not rely on equipment operating conditions, equipment models, or basic environment. By combining quantitative feature matrices + multi-dimensional reconstruction + algorithm engineering, it achieves equipment fault prediction without differentiation, simple fault type classification, effectively balancing model lightweight and generalization capabilities, realizing "plug-and-play" models. It introduces a reinforcement learning mechanism based on fault type classification. With deeper application platform use, the model's "generalization capability" will become stronger.

5. Advantage - Quantitative Diagnosis

The generalized algorithm is paired with a parallel module called "Quantitative Diagnosis," which can monitor model accuracy, precision, recall, cluster shifts in quantitative feature matrix parameters, and data balance of label data. It updates model versions through self-learning and self-adaptive methods, with update granularity reaching the network structure level.

6. Intelligent Early Warning

Includes three major model types: dynamic threshold, trend prediction, and correlation analysis. The data objects are feature values collected by sensors or robust feature values extracted during preprocessing, used to determine equipment fault status. Unique processing methods effectively avoid alarm storms and false alarms.

7. Fault Diagnosis

Includes mechanism models and AI models. Mechanism models are based on vibration principles, extracting time-frequency domain feature matrices for rule matching. AI models are based on deep learning and big data artificial intelligence algorithms, showing significant advantages in mechanical fault diagnosis. Using transfer learning + reinforcement learning effectively solves the problem of missing fault data in industrial scenarios.

8. Large Model Maintenance Recommendations

Confirmed equipment alarms enter the work order process. The large model can provide professional maintenance guidance based on full equipment information, alarm records, maintenance records, combined with general large model knowledge.

9. Equipment Health Assessment

Combines basic equipment information, performance indicators, and current alarm indicators for empirical weighting, using large model technology to assess equipment health.

10. Equipment Remaining Life Prediction

Combines basic equipment information, performance indicators, and current alarm indicators for empirical weighting, using large model technology to evaluate the remaining life of equipment.

11. Two-Optimal Algorithms

Uses usage optimization and design optimization. Combines basic equipment information, performance indicators, current alarm indicators, health indicators, remaining life, and historical full information for joint spatial processing, providing usage optimization and design optimization suggestions with large model technology.

12. Zero Inventory

Accurately predicts demand by analyzing historical data, equipment information, fault diagnosis information, production information, and online monitoring information through deep learning; dynamically adjusts inventory levels using optimization algorithms, setting safety stock and replenishment strategies; intelligently schedules logistics, optimizing routes and delivery timeliness to ensure immediate replenishment; AI monitors supply chain anomalies in real-time, quickly responding to demand surges or supply delays, reducing backlog and stockout risks, and minimizing inventory costs.

1. Evaluation Method

Using black-box testing, the testing is based on the fundamental principles of fairness and impartiality. The basic process involves a third-party organizer providing unlabeled data, the participants providing fault diagnosis results, and the statistical metrics include intelligent early warning accuracy, intelligent diagnosis accuracy, recall rate, and F1-score.

2. Evaluation Honors