Air Compressor Bidirectional Fault Diagnosis and Remote Control System Utilizing Big Data

摘要

The present invention relates to a two-way fault diagnosis and remote control system for air compressors using big data, and more specifically, to provide information on the state, including energy usage information, air quality information, efficiency information, temperature information, etc. of multiple air compressors to the site. It is collected from the installed automatic compressor controller (MACS) and provided to a remote control server configured at a remote location. It uses the status information received from the remote control server to diagnose the presence or absence of air compressors, and the possibility of failure and A/S for each air compressor. Two-way fault diagnosis and remote control of the air compressor using big data to identify the timing in advance and transmit the status information on the air compressor with possible failure to the on-site person in charge to perform A/S prior to the failure. It's about the system. According to the present invention, status information including energy consumption information, air quality information, efficiency information, temperature information, etc. of a plurality of air compressors is collected by a compressor automatic controller (MACS) installed in the field and provided to a remote control server configured at a remote location. , Diagnose the presence or absence of air compressors by using the status information received from the remote control server, and identify the possibility of failure and A/S time for each air compressor in advance to provide status information about the air compressors that may have a failure. By transmitting to the person in charge of the person in charge and exerting the effect of performing A/S prior to the failure, it provides the advantage of preventing an emergency situation in which the actual air compressor fails and cannot be used for a long time. In addition, by collecting big data called state information for each air compressor, and predicting the possibility of failure using an artificial intelligence algorithm using the collected big data, it has an effect of further increasing the accuracy of the possibility of failure.