Methods and systems for intelligent monitoring of equipment damage based on mechanism and operating condition big data

A method for intelligent monitoring of equipment damage based on mechanism and operating condition big data, wherein the method is used for monitoring damage modes of pressure equipment, the method comprises:

• setting a plurality of damage modes of the pressure equipment, building mechanism model samples for each damage mode based on a technical standard and engineering cases, and building operating condition big data samples for the each damage mode based on field data;
• before building the operating condition big data samples, building a custom dictionary and a stop words list related to the plurality of damage modes of the pressure equipment;
• wherein building the mechanism model samples and building the operating condition big data samples includes:
• obtaining the mechanism model samples describing the each damage mode from the technical standard and the engineering cases, and obtaining operating condition data samples describing the each damage mode from the field data;
• cleaning, segmenting, filtering stop words, and deduplicating first text samples obtained from the technical standard according to the custom dictionary and the stop words list to obtain a technical standard keyword library;
• simulating a mechanism model recognition manner and expert experience, obtaining self-generated technical standard keyword samples according to the technical standard keyword library, and jointly building the mechanism model samples with second text samples obtained from the engineering cases;
• and
• retaining operating condition parameters of preset types in the operating condition data samples and deleting operating condition parameters of other types to obtain the operating condition big data samples;
• using the mechanism model samples and the operating condition big data samples as a dataset for model training, respectively, to build a mechanism model and an operating condition model;
• and
• inputting real-time text data to be predicted and real-time operating condition data into the mechanism model and the operating condition model respectively, calculating a first possibility that the real-time text data belongs to each of the plurality of damage modes, and a second possibility that the real-time operating condition data belongs to the each of the plurality of damage modes, generating an equipment damage monitoring result based on the first possibility and the second possibility;
• a first possibility set P(1) that the real-time text data belongs to the each of the plurality of damage modes is expressed as:
• P(1)={P11, P12, P13, . . . , Pin};
• a second possibility set P(2) that the real-time operating condition data belongs to the each of the plurality of damage modes is expressed as:
• P(1)={P21, P22, P23, . . . , P2n}, where P1n denotes a first possibility that the real-time text data belongs to a n-th damage mode;
• and Pn2 denotes a second possibility that the real-time operating condition data belongs to the n-th damage mode;
• multiplying the first possibility and the second possibility that the real-time text data and the real-time operating condition data belong to a same damage mode to obtain a comprehensive possibility set {P1, P2, P3, . . . , P)} that the pressure equipment belongs to the each damage mode;
• wherein P1=P11×P21, P2=P12×P22, P3=P13×P23, . . . , Pn=P1n×P2n;
• wherein, sorting possibilities in the first possibility set P(1), possibilities in the second possibility set P(2), and possibilities in the comprehensive possibility set, respectively, from large to small, and taking top-ranked damage modes and possibilities of the top-ranked damage modes as the equipment damage monitoring result.