Marine platform air compressor fault diagnosis method

Abstract

The invention provides a marine platform air compressor fault diagnosis method and belongs to the technical field of marine engineering equipment fault diagnosis. According to the marine platform aircompressor fault diagnosis method, marine platform air compressor monitoring signals are firstly divided into three types of temperature signals, pressure signals and mechanical signals, and marine platform air compressor signal collection points are provided with data sensors correspondingly to collect working signals; linear filtering method Kalman filtering is used at a data processing stage torealize multi-sensor data fusion, and fused data are further divided into a training set, a verification set and a test set; and after logical construction is performed on a Rattan Bayesian classifier, likelihood probability control parameters are optimized by the training set data, the complete marine platform air compressor fault diagnosis method is finally obtained, and fault diagnosis can berealized after importing actually-measured data.

Description

technical field [0001] The invention relates to a fault diagnosis method for an air compressor on an ocean platform, and belongs to the technical field of fault diagnosis of ocean engineering equipment. Background technique [0002] With the rapid development of human society, the human demand for oil and gas resources has increased sharply, and the scale of oil and gas exploitation has also been expanding day by day, showing a development trend from road to sea. Offshore oil and gas exploitation takes the offshore platform as the main carrier. Its system design is complex and closely connected, and there are various types of equipment. Under the harsh working environment at sea, different types of failure modes are prone to occur. The failure mode is likely to expand to other types of failure modes, thereby affecting the production safety of the platform, and in severe cases, it will cause major economic and personnel losses to the entire platform. The proposal and applicat...

AI Technical Summary

Problems solved by technology

However, the above-mentioned methods have certain shortcomings, such as low raw data processing efficiency and difficulty in signal analysis and modeling, which greatly limit the fidelity of fault signal collection and fault diagnosis accuracy, increase the rate of misdiagnosis, and lay a solid foundation for equipment maintenance and maintenance. Health management brings many uncertainties

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