How Neuron soundware proved its worth in potato chip production
Neuron soundware is sometimes compared to conventional vibrodiagnostics. Our experience with the oil pump for frying traditional Czech potato chips proved that our diagnostics technology using sound and AI is more sensitive and able to alert staff to potential problems in the machinery earlier than conventional methods.
Bramburky.cz has problems multiple times a year with the pump frame twisting. The pump frame has screw-on feet for a fully adjustable height, but they get loose and twist the frame from time to time. This leaves the pump misaligned, eventually destroying it and causing production delays.
The production manager wants to avoid these delays, because every pump change puts production back for up to a day, with financial losses of thousands of euros. We set up both technologies, conventional vibrodiagnostics and Neuron soundware diagnostics using sound and AI, to find a recommendation for which diagnostic method the potato chip company owner should use for this type of machine.
How did this comparison of conventional vibrodiagnostics and innovative AI-supported sound diagnostics turn out?
- April 14, 2021 – we installed measuring points and took initial measurements for the equipment in normal operations (NSW + vibro)
- April 27, 2021, at 8:45 p.m. – we took new vibration and sound readings before testing the loosened screw on the machine foot
- April 27, 2021, at 9:00 p.m. – we loosened the screw first by 90°, then by 180°, and finally by 420°.
At every step, we took vibrodiagnostics measurements and continually collected data via online Neuron soundware AI-assisted sound monitoring
The images below show clearly that Neuron soundware responded at the first 90° turn of the screw and reported an anomaly. Conventional vibrodiagnostics, in compliance with the usual ISO 10816-3, would have waited significantly longer to send the alert.
The vibrodiagnostics system just barely reached the warning level in the second phase of the test, when the frame was already significantly twisted and the pump seal was already starting to leak.
In the third, extreme phase of testing, vibrodiagnostics also showed a value only slightly above the alarm threshold, even though the pump seal was leaking practically everywhere and the elastomeric coupling member had already started significantly degrading.
We hardly need to give a long explanation of the major advantage is the speed of transmitting this information in particular. While vibration analysis is performed by an expert with set working hours, Neuron soundware works online through HW, SW, and AI continuously and remotely.
Fig.: recording of sound at anomaly level 0.5 as compared to the nominal state, when Neuron soundware begins sending an alert (notification) of an impending fault.
Fig.: Excerpt from ISO 10816-3. This shows that the pump measurement notification values for are 4.5 mm/s and alarm values are 7.1 mm/s
Fig.: Overall level of vibration measurement speed according to ISO 10816-3. In ordinary machine operations, these values range from 2-2.5 mm/s. In phase one of the test, they were 2.9 mm/s, in phase two 4.57 mm/s, and in phase three 8.14 mm/s.
How Neuron soundware works and why it can detect machine changes so quickly
The technology works on proven principles of technical diagnostics aided by modern AI and machine learning methods. Once the sensors have been placed on the machine, the system records initial data on machine operations and saves it as normal (nominal).
Preset data from the “machine library” is then added to the nominal data for training purposes. The system then compares this data with the data measured during machine operation. If the sound of the machine significantly deviates from its nominal state or the sound matches any of the anomalous sounds from the machine library, the Neuron soundware system reports an anomaly. In other words, it sends a notification that something unusual is happening with the machine and that a technician should go and check or repair the affected part of the machine.
What is the most effective way to prevent faults in industrial machines? The key is to install IoT equipment and sensors on a critical machine while it is still running properly. This ensures that the data collected will truly reflect the usual values for the operation of that machine. We call this training the AI model and it typically goes on for several days. The model is then deployed immediately on the selected machine, adding technical knowledge of the specific machine to the continuous monitoring. The alerts allow staff to respond quickly and decide on the appropriate response to minimize losses. Most alerts allow maintenance staff to plan their operations to avoid unplanned restrictions in production.
Starting immediately, when the monitoring solution is deployed on the machine, the customer receives the service of remote equipment monitoring. They know whether the machine is up and running or experiencing a failure, and they have access to data for their own analyses, inspections, and assessments of machine operations. After deploying the trained model, the customer receives information on machine anomalies including recommended steps to verify the impact on machine operations or to repair the problem. Over time, these recommendations will be more detailed and precise, even without validation from an expert human diagnostician.
The service’s value to the customer grows over time. The more faults it registers, the better the AI can distinguish between faults and give staff more precise information on the defect and more time to plan inspections or repairs.
Production directors who make this investment have a clear advantage over others, as they can make better use of their expert staff, respond more quickly to nip machine problems in the bud, and create data and knowledge bases for future advanced AI models.
Since fewer experts are available on the market and pressure on innovation and digitalization is growing, this is the time to invest in AI-assisted monitoring.