Industrial Problem Solution
Development of Mathematical Fault Detection Algorithm for the Automatic Control Big Data Analysis System of Mechanical Facilities
2020-06-151. Company introduction
Seoul Metro, which used to operate subway lines 1 to 4, and Seoul Metropolitan Rapid Transit Corporation, which operated lines 5 to 8, were established by integrating them.
2. Problem Background and Summary
The industrial problem is to improve the Smart Automatic Mechanical Big Data Analysis-system (SAMBA) established by the Seoul Transportation Corporation.
The goal is to enhance algorithms that automatically recognize mechanical failure situations or predict failures through a mathematical approach.
Develop a way to efficiently utilize data stored in real-time on big data servers
Development of mathematical algorithms to detect abnormalities in facilities such as V-belt and motor bases and predict failures
3. Solving Process
Proposal of data collection methods for detecting abnormalities in air conditioners, such as V-belt slip and departure: Securing data (abnormal data) in case of a failure is essential to conducting guidance studies such as deep learning or evaluating the performance of a model. We decided to select specific air conditioners to develop abnormal detection algorithms and expand them. Collect abnormal data by simulating failure of 10 air conditioners in Janghanpyeong Station.
Visualize and interpret sensing data using time-series data synchronization techniques: To develop a model that detects anomalies, an algorithm is applied to divide time series data in one dimension into data preprocessing based on the point at which the air conditioner motor operates. Suggest synchronization method with correlation, Show how to sync with maximum values, Visualize and interpret data
4. Ripple effects and future plans
If applied to the Seoul Metro's automated big data analysis system, it is expected to provide practical convenience for citizens to use the subway.
We are planning to develop abnormal detection algorithms that can be applied to various air conditioners in common, not to individual air conditioners only.
Anomaly detection model will be developed using vibration data, including a comparative analysis of current sensing data and vibration data.
1. Company introduction
Seoul Metro, which used to operate subway lines 1 to 4, and Seoul Metropolitan Rapid Transit Corporation, which operated lines 5 to 8, were established by integrating them.
2. Problem Background and Summary
The industrial problem is to improve the Smart Automatic Mechanical Big Data Analysis-system (SAMBA) established by the Seoul Transportation Corporation.
The goal is to enhance algorithms that automatically recognize mechanical failure situations or predict failures through a mathematical approach.
Develop a way to efficiently utilize data stored in real-time on big data servers
Development of mathematical algorithms to detect abnormalities in facilities such as V-belt and motor bases and predict failures
3. Solving Process
Proposal of data collection methods for detecting abnormalities in air conditioners, such as V-belt slip and departure: Securing data (abnormal data) in case of a failure is essential to conducting guidance studies such as deep learning or evaluating the performance of a model. We decided to select specific air conditioners to develop abnormal detection algorithms and expand them. Collect abnormal data by simulating failure of 10 air conditioners in Janghanpyeong Station.
Visualize and interpret sensing data using time-series data synchronization techniques: To develop a model that detects anomalies, an algorithm is applied to divide time series data in one dimension into data preprocessing based on the point at which the air conditioner motor operates. Suggest synchronization method with correlation, Show how to sync with maximum values, Visualize and interpret data
4. Ripple effects and future plans
If applied to the Seoul Metro's automated big data analysis system, it is expected to provide practical convenience for citizens to use the subway.
We are planning to develop abnormal detection algorithms that can be applied to various air conditioners in common, not to individual air conditioners only.
Anomaly detection model will be developed using vibration data, including a comparative analysis of current sensing data and vibration data.