Device Predictability Analysis

Background and Challenges

Numerous enterprises in the field of big data pay attention to predictive maintenance applications. Using the past equipment and threshold data, real-time warning is possible with the expert rules analysis of the status of the equipment.
In the AI field, it is possible to build more samples through more hyperdimensional through machine learning, and perform predictive analysis of AI on machines and equipment.

Business Goal

The building of the machine health model provides a scientific basis for the directional maintenance of equipment failures and the development of maintenance plans.

Difficulties and Key Success Factors

1. Collect data related to the daily operation status of the equipment and analyze the factors affecting the equipment;
2. Consider the condition of the centralized control center informatization at the early stage and carry out the feature engineering construction and design a matching modeling scheme.

Business Outcomes

1.4Paradigm’s ‘Sage’ platform covers the whole process of machine learning. Through automated machine learning, users can quickly get started without an in-depth understanding of the algorithm principles, providing a truly ‘easy to use’ artificial intelligence platform.
2.The Sage platform has powerful computing capabilities, ease of use and fast operations and rich data processing, and reduces the time cost associated with data preprocessing, and achieves rapid modeling iteration attempts in a limited time.