1. General-Purpose Equipment Model

The fault model primarily focuses on components at the equipment level for machinery and pump systems (such as bearings, gears, etc.), using a single-channel signal for analysis. The main processing steps include loading the configuration table, data reading, feature extraction and preprocessing, applying the algorithmic model, and finally outputting the conclusions to the application layer.

2. Multi-sensor Fusion Model

Primarily focused on equipment diagnostics for large-scale units such as compressors and internal combustion engines, the model takes multi-channel sensor data as input and integrates data from multiple sensors to enable intelligent fault diagnosis and decision-making. The main processing steps include loading the configuration table, reading multi-channel data, constructing a feature matrix, applying the algorithmic model, and finally delivering the diagnostic conclusions to the application layer.

3. Multiple Model List

We have completed the research and training of 113 fault diagnosis algorithm models, which are now ready for direct deployment and use—eliminating the need for on-site learning or retraining. These algorithms cover dynamic and static electrical instruments across industries such as building materials, petrochemicals, metallurgy, non-ferrous metals, energy, coal mining, home appliances, and automotive manufacturing. Official evaluations show the software’s fault diagnosis algorithms achieve an accuracy rate of 96%, while customer statistics indicate an average accuracy of 75%. When combined with human expertise, the overall accuracy rises to an impressive 98%.

4. Advantage – Generalization Algorithm

Without relying on the operating conditions of the equipment, its specific model, or even the underlying environmental setup, this approach combines a quantitative feature matrix, multi-dimensional reconstruction, and algorithmic engineering to achieve seamless, non-discriminatory prediction of equipment failures. It also enables simple yet effective classification of fault types, striking an optimal balance between model lightweighting and generalization capability. As a result, the model can be seamlessly integrated into existing systems—“plug-and-play.” Moreover, by incorporating a reinforcement learning mechanism tailored to distinct fault categories, the model’s “generalization ability” will continue to strengthen as it is increasingly deployed across diverse application platforms.

5. Strengths – Quantitative Diagnosis

The generalization algorithm is paired with the parallel module "Quantitative Diagnosis," which monitors the model's accuracy, precision, and recall. It also tracks clustering shifts in the quantized feature matrix parameters and ensures balanced distribution of label data. Through self-learning and adaptive methods, the module facilitates seamless model version updates—reaching a level of granularity that even extends to the network architecture itself.

6. Intelligent Early Warning

It includes three major categories of models: dynamic thresholding, trend prediction, and correlation analysis. The data objects are either feature values collected by sensors or robust features extracted through preprocessing, all used to determine the fault status of equipment. Thanks to a unique processing approach, it effectively avoids alarm storms and false alarms.

7. Fault Diagnosis

Including mechanistic models and AI models. The mechanistic model is based on vibration principles, extracting feature matrices from the time-frequency domain for rule-based matching. Meanwhile, the AI model leverages deep learning and big-data-driven artificial intelligence algorithms, demonstrating significant advantages in the field of mechanical fault diagnosis. By combining transfer learning with reinforcement learning, it effectively addresses the challenge of missing fault data in industrial scenarios.

8. Large Model Maintenance Recommendations

Confirmed device alerts will trigger the work order process, and the large-scale model can provide expert repair guidance by leveraging comprehensive device information, alert records, and maintenance history, combined with its extensive general knowledge.

9. Equipment Health Assessment

By integrating the equipment's basic information, performance metrics, and current alarm indicators through an empirical weighting process, we leverage advanced large-model technology to assess the equipment's health status.

10. Equipment Remaining Life Prediction

By integrating the equipment's basic information, performance metrics, and current alarm indicators through empirical weighting, we can leverage advanced large-model technology to assess the equipment's remaining lifespan.

11. Two-Opt Algorithm

Utilize optimization and design optimization. Combine the device's basic information, performance metrics, current alarm indicators, health status metrics, remaining lifespan, and historical full-data records for joint spatial processing. Then, leverage advanced large-model technologies to provide optimized usage and design recommendations.

12. Zero Inventory

By leveraging deep learning to analyze historical data, equipment information, fault diagnosis insights, production details, and online monitoring data, we accurately predict demand. Combined with optimization algorithms, we dynamically adjust inventory levels, establishing safety stock levels and implementing replenishment strategies. Intelligent logistics scheduling further optimizes routes and delivery times, ensuring timely restocking. Meanwhile, AI continuously monitors the supply chain for anomalies, enabling rapid responses to sudden demand shifts or supply delays—ultimately minimizing the risks of overstocking and stockouts, while driving inventory costs to their lowest possible level.

1. Evaluation Method

Black-box testing is employed, with tests conducted based on the fundamental principles of fairness and impartiality. The basic process involves a third-party organizer providing unlabeled data, while contestants submit their fault diagnosis results. Key metrics include intelligent early-warning accuracy, intelligent diagnosis accuracy, recall rate, and F1-score.

2. Evaluation Honors