For a long time, factory managers have developed many analytical models and rules of thumb to predict machine equipment failures, reduce maintenance costs, and increase plant productivity; however, with the rapid evolution of artificial intelligence in recent years, future implementation of machine equipment predictions In the maintenance work, the application of machine learning calculus technology will be able to achieve twice the result with half the effort. In traditional industry practice, companies such as M2M Data and Senseye develop physical analysis models based on data collected from client devices, including pressure, motor speed, sound and temperature. The parameters of the type, in the analysis model, if the above parameters deviate from the normal values, they are sending a message to the manager that the machine may be abnormal.
Looking ahead, several startups, including Otosense, 3DSignals, Predikto and Mtell, use machine learning algorithms to find specific patterns in the above data and link these patterns to the abnormal conditions of the machine, although these algorithms may not Built on the actual model of any machine running, but still able to detect abnormal values ​​that deviate from the acceptable baseline while the machine is running. The advantage of machine learning algorithms is that as long as a single parameter is used, multiple different modes of operation can be derived in a single device, just as astronomers use machine learning techniques to separate light data from different sources in the same universe. In order to determine whether the illuminant belongs to a galaxies, quasars, planets or galaxies.
The above-mentioned astronomers' practices can also be applied to the predictive maintenance of machinery and equipment, but unlike astronomers who use light data, factory managers collect sound data through sound Sensors installed on machinery. The machine learning algorithm is then used to distinguish between different sound sources in the field. The current machine learning algorithms can be roughly divided into two categories, one is a monitoring mode in which field personnel manually annotate the analysis data, but the limitation of this method is that its operation must rely on training computer learning. The amount of annotation data available, and because the model must be fine-tuned to accommodate the available machine data, this also makes the system's accuracy prone to instability.
As for another non-monitoring mode that does not require manual annotation of data, it is more like a computer to explore in the dark than the monitoring mode. Although this method does not clearly explain or understand what abnormal conditions occur in the machine, However, it is possible to alert the management to any abnormal data.
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