Intelligent Grain, Oil and Food Processing Equipment: AI-Based Fault Diagnosis and Predictive Maintenance

The rapid development of AI technology has made its application in the grain, oil and food processing industry more and more extensive, especially in equipment fault diagnosis and predictive maintenance shows great potential. The traditional maintenance of food processing equipment often relies on empirical judgment and regular maintenance, which has the disadvantages of lagging response, high maintenance cost and long downtime. AI-based fault diagnosis and predictive maintenance technology, however, collects equipment operation data in real time through an integrated sensor network, and analyzes the data characteristics using machine learning algorithms to realize real-time monitoring of equipment status and fault prediction. This paper introduces the basic principles of AI in fault diagnosis, including key steps such as data collection and processing, feature extraction, model building and prediction result evaluation. In addition, this paper analyzes how AI can identify potential failure modes and formulate maintenance plans in advance by analyzing equipment operation data, thus effectively reducing unplanned downtime and maintenance costs. AI-based fault diagnosis and predictive maintenance technology has revolutionized the grain, oil and food processing industry, which not only improves production efficiency and guarantees product quality, but also significantly reduces maintenance costs. The research results of this paper have practical value for promoting the intelligent upgrading of the grain, oil and food processing industry.