Data-driven marine diesel engine fault detection method

A marine diesel engine, fault detection technology, applied in the direction of internal combustion engine testing, etc., can solve the problems of high time and labor costs, low real-time performance and detection efficiency, and difficult measurement

Active Publication Date: 2020-06-16
PEKING UNIV
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Problems solved by technology

The above technologies either require high-precision monitoring of parameters, which is difficult to measure; or the model constructed has a small scope of application; or requires experienced technicians to perform data analysis, which has high time and labor costs, real-time performance and detection low efficiency

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  • Data-driven marine diesel engine fault detection method
  • Data-driven marine diesel engine fault detection method
  • Data-driven marine diesel engine fault detection method

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Embodiment Construction

[0059] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0060] figure 1 The structure of the diesel engine fault diagnosis system provided for the concrete implementation of the present invention mainly comprises KPI modeling unit and fault detection unit; KPI modeling and fault detection method flow process are respectively as follows figure 2 , image 3 shown. In specific implementation, the KPI modeling algorithm based on the improved PLS is as follows:

[0061] 1. Collect historical data and construct a data matrix Y∈R m×N and Θ∈R l×N ;

[0062] 2. Use the standard PLS method to solve the regression coefficient M of Θ and Y;

[0063] 3. Implement singular value decomposition:

[0064] 4. Decompose the process variable space into two orthogonal subspaces:

[0065] 5. Design T 2 Statistics: threshold

[0066] 6. Detect faults accordin...

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Abstract

The invention discloses a data-driven marine diesel engine fault detection method. The method comprises the following steps of clustering working modes of historical data of a diesel engine by adopting a k nearest neighbor clustering method to obtain a working mode type of the diesel engine; establishing a KPI model corresponding to each working mode by adopting a partial least squares regressionmethod PLS, and calculating an inspection threshold value; inputting real-time data to be detected; matching the working modes of the diesel engine by judging whether a distance between a real-time data point to be detected and a clustering center of the historical data is smaller than a preset threshold d or not; inputting to-be-detected data into the KPI model of the corresponding mode, and performing fault test statistics calculation to obtain KPI fault test statistics; and realizing data-driven marine diesel engine fault detection through comparison. By adoption of the technical scheme, real-time, wide-application-range and high-efficiency online fault diagnosis can be achieved.

Description

technical field [0001] The invention relates to the field of marine diesel engine detection, in particular to a data-driven marine diesel engine fault detection method based on a partial least squares regression method. Background technique [0002] Diesel engine is the power heart of the ship, once it breaks down, it will inevitably affect the operation and safety of the ship. According to statistics, marine accidents of ships are mainly caused by mechanical failures, and diesel engine failures account for 45% of mechanical failures. Traditional diesel engine fault diagnosis techniques include thermal parameter method, oil analysis method, vibration analysis method and instantaneous speed method. The thermal parameter method mainly uses the change of the thermal parameters (dynamometer diagram, pressure, temperature, etc.) of the marine diesel engine to judge its working state; the oil analysis method is to analyze the performance of the diesel engine's lubricant (or worki...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M15/05
CPCG01M15/05
Inventor 杨莹何志晨李鹤张瑀涵刘瑞杰
Owner PEKING UNIV
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