Monitored machine: industrial pump motor assembly
Dynamox develops a wireless vibration and temperature data logger with Mobile App, Web Platform, and Gateway (optional) to monitor the condition of industrial machinery.
Recently the solution was installed in a large textile company in São Paulo (Brazil), in which one of the monitoring points is a motor bearing at coupling end (CE).
During monitoring, some changes in vibration and temperature patterns were observed, both in terms of continuous data analysis, as well as spectral and waveform analysis.
The vibration and temperature trend analysis at the monitored points usually gives a first indication for the detection of anomalies. With continuous monitoring (with a sampling interval of 10 min, for example) small gradients and sudden increases may be apparent. Tools, such as the moving average, can help in visualizing the trend of the collected data, smoothing slight variations and highlighting the large ones, as can be seen in the following images.
As can be seen in the images, there was a noticeable increase in the pattern of vibration, especially in the radial direction, represented by pink curve in the graphic. This event also manifested itself in terms of temperature, as shown in the following figure.
Note: The device has been installed with a magnetic base, which can make it difficult to detect these temperature changes. Nevertheless, the sensor identified a significant pattern change. Screw mount is recommended for more reliable data collection.
In general, the second step in the assessment of anomalies is spectrum-based analysis. With proper resolution and dynamic range, it is possible to identify the defect.
The following image makes a comparison between two triaxial spectra, one realized before the fault, represented by blue, pink and yellow curves, and another one realized after the appearance of the anomaly, in gray.
It was detected mainly increase of the peaks related to BPFI (Ball Pass Frequency Inner Race) and BPFO (Ball Pass Frequency Outer Race) associated with a rise on the speckled carpet.
Another important tool that has information about the characteristics of the defects is the waveform in the time domain, especially in cases where there are impulsive and non-stationary characteristics. The main impact of the defect is a change in the waveform, with the presence of periodic pulses, increasing RMS and peak-to-peak values, for example. Besides, typically the signal of a new bearing has a noisy characteristic with low amplitude, no pulses present, and statistically close to white noise.
In the following images, you can see the increase of these values. The first image shows the waveform before the defect, and the second occurs after the fault was detected.
Evidence of detected bearing failure
Based on the data mentioned above, the company’s Maintenance Team intervened in the machine and verified the presence of a defect in the bearing, which showed electric current, evidenced by the distinctive marks on the bearing tracks, as shown below. On the other hand, these marks may also be indicative of the health of the machine as a whole, leading to other possible problems such as grounding problems, electrical failure, overload or short circuit, etc. An investigation with the cross-checking of information and history of the machine is essential at this stage.
Analysis after rolling change
After rolling bearing replacement, a significant decrease in vibration levels can be seen, both in terms of continuous monitoring, as well as spectral and waveform analysis.
The following image shows in continuous vibration terms this decrease in levels.
This is all the more visible when comparing the vibration spectra of during and after the bearing change. The acceleration spectra of the damaged bearing is shown in gray in the figure below. The spectrum after the rolling bearing replacement is represented blue, pink and yellow curves. Note that the significant reduction in vibration level in terms of BPFI (Ball Pass Frequency Inner Race) and BPFO (Ball Pass Frequency Outer Race) related peaks and on the carpet levels (also called spectra noise floor).
In conclusion, this reduction in levels can also be seen in the waveform, presented on the same scale in the figures below.
Table summarizing the signal parameters of the acceleration time series (g):
From this moment, we will post more success cases on our Blog, and examples of companies that have been increasing the reliability of their machines with our solution.
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