A new revolution in motor maintenance under the trend of the Internet of Things, the more secure it is, the more secure it is.
At present, the largest application of the Internet of Things in the industrial field is predictive maintenance. It is difficult to achieve continuous operation of the production line for 24 hours a year. However, today, predictive maintenance can solve equipment hazards in advance, thus preventing equipment downtime. . With the rise of the Internet of Things, big data and AI technology, factory equipment has ushered in a new revolution. More and more machines are connected to the Internet, enabling remote data monitoring and analysis. This scenario has made it easier for staff. Efficient work. However, many people are unfamiliar with this model, blindly following the trend may not get the desired effect, then how can companies combine the Internet of Things, big data, and enhance the value of the factory? At present, the largest application of the Internet of Things in the industrial field is predictive maintenance. It is difficult to achieve continuous operation of the production line for 24 hours a year. However, today, predictive maintenance can solve equipment hazards in advance, thus preventing equipment downtime. . Electric motors are the main force in today's industry. There are many motors for various purposes in the factory. For example, electric motors are used in equipment such as lifting, stamping, handling, dust removal and drying. The maintenance of the motor has become an important issue in the factory, especially in some harsh environments, users are more inclined to remote maintenance. Motor efficiency degradation is the most common. If hundreds of motors in the plant are degraded, this has a large impact on plant production and the risk of downtime. The failure of one motor on some production lines can cause the entire production line to shut down, which is very expensive. To reduce unplanned downtime, the factory employs maintenance personnel. But traditional maintenance methods are also expensive because they have no better way to predict the future of the equipment and still cannot avoid downtime. Usually the motor has the following maintenance methods: Maintenance after failure: It means to wait for the motor to stop after the maintenance, this is usually the case that the motor has been damaged and needs to be replaced again, because it is not a simple matter to repair the motor on site. Can be replaced first to ensure that production can continue. Preventive maintenance: In order to avoid complete failure of the motor, the staff will maintain it according to the average running time of the motor. Often for safety reasons, maintenance happens too early, and parts are still in good condition and may be replaced. This approach does not guarantee that new problems will not occur after maintenance. Condition monitoring maintenance: Usually, there are some phenomena before the motor stops, such as noise, vibration, and uneven speed. The method of condition monitoring and maintenance is to monitor each motor, similar to the doctor's stethoscope, and the maintenance personnel determine whether maintenance is needed after on-site diagnosis. On-site inspection can be a dangerous task, and maintenance personnel must travel anywhere in the factory. With the development of Internet of Things technology, these traditional maintenance methods will become a thing of the past. The plant will be equipped with one or more sensors for each motor that are connected to the control database to continuously collect data about the motor. In the database, artificial intelligence is used to learn the behavior of each motor, and an alarm is generated immediately when the motor deviates from normal. This sensor-based data collection is more accurate and thorough than any manpower test, because many of the motor's signs are difficult to identify with the eyes and ears, but the IoT sensors can detect small changes in the device. The combination of the Internet of Things and artificial intelligence not only sees problems, but also scans and detects possible problems. This is called predictive maintenance. This approach is very good to avoid the occurrence of faults, while letting maintenance personnel know when the best maintenance time, not too slow or too early. At the same time, depending on the severity of the problem and alarm, even the downtime of the motor can be planned to minimize interference with the operation. In the operation of the factory, wrong decisions or decisions that are too slow and too early will bring a lot of waste of resources and money. The Internet of Things helps users collect data that has never been captured in the past, and through artificial intelligence analysis, learn from it and make better decisions faster.
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