Avoiding Unplanned Downtime: Online
Keeping a close eye on the condition of critical equipment is fundamental in any industrial facility. When critical bearings fail, it almost always leads to unplanned downtime and interrupted production process, costing companies thousands in production loses.
In this case study we will look at how an online monitoring solution using ultrasonic sensors was able to detect an issue on a critical bearing before it turned into a big problem.
Critical equipment: bleach decker in a pulp and paper plant
Usually, in pulp and paper plants we will find a wash floor or wash area, where the paper comes through to be thoroughly cleaned / bleached. That job is done by a machine called a bleach decker, which is considered a critical and fundamental piece of equipment for production operations.
In this particular plant, which has a predictive maintenance program in place, it was decided to invest in online monitoring for these machines. The maintenance team wants to be alerted as soon as anything unusual is happening with the equipment in order to prevent any failures that would lead to a stop in production. This machine has 4 bearings, of about 48 inch / 120 cm diameter, rotating at 3 RPM.
To enable online monitoring and early failure detection, ultrasonic sensors are being used on the machines’ bearings. These are UE Systems Remote Access Sensors, which are permanently installed on the bearings and constantly collect decibel readings and sound recordings. All this data is then sent to a central processing unit called the 4Cast. This unit is connected to the Internet and will alert the maintenance team (e-mail and SMS alerts) when certain decibel levels are reached.
The preference for ultrasound technology to monitor these bearings has to do with its obvious advantages: since the ultrasonic sensors monitor the bearings’ friction levels, any increase in friction will be noticed. This allows for a very early warning of failure. Also, because the data from the sensors comes in the form of decibel readings, it is easy to interpret: the higher the friction, the higher the dB value. When this value reaches a certain limit above the baseline, an alarm is sent.
And, even more relevant to these bearings, ultrasound is the most efficient technology to inspect slow speed bearings. The bearings on this machine are rotating at 3RPM.
At such slow speeds, it is generally extremely challenging to notice any issues using technologies such as vibration analysis or thermography. But ultrasound shines when the subject is slow speed bearing monitoring, especially when you can record the sound from the bearing, analyse it in a sound spectrum software and check if the amplitude shows any peaks, which normally indicate a fault.
Thus, ultrasound is the perfect technology when we want to online monitor slow speed critical bearings.
Failure detection with online monitoring using ultrasonic sensors
Everything seemed to be fine with the bleach decker at this pulp and paper facility, as the machine was working as expected. However, the 4Cast, an ultrasonic online monitoring system, received an unusual decibel reading from one of the ultrasonic sensors. The NDE (non-drive-end) bearing of this bleach decker was registering 17dB when, normally, a bearing rotating at such slow speeds like 3RPM should simply show a 0dB reading.
This, of course, triggered the system to immediately alert the maintenance team. The 4Cast was setup to consider any reading above 8dB on this bearing to be a high alarm, and therefore, the following alert was sent from DMS, the UE Systems software where all the data from the 4Cast is stored:
We can clearly see why the alert was triggered: the 4Cast received a 17dB reading from a bearing where the threshold for a high alarm was setup at 8dB. The alert message also contains useful information regarding the machine (operating floor slow moving bearing; bleach decker) and, naturally, a time stamp of when the reading was taken.
When an alarm level is reached, the 4Cast will also take a sound recording from the bearing for further analysis. This is especially useful in slow speed bearings, where the sound spectrum can tell us a lot about what’s going on with the asset. In this case, and even though the machine was apparently working as expected, the sound file spectrum showed a very different story.
The peaks shown in this sound sample clearly indicate a problem with the bearing. Also, when reproducing the sound file, we could very clearly hear the impact noises. The failure was even more obvious when the sound file was compared to a sound recording from one of the other bearings.
We can clearly see the differences. In this case, the recording sounds smooth and looks uniform, and we don’t see amplitude peaks at all. So, this would be an example of how the sound spectrum of a good bearing should look like.
The next step for the maintenance team was scheduling the replacement of this bearing, without disrupting production. When the bearing was dismantled, the damage was clearly visible.
The signs of impact are obvious. Also, metal fragments were found in the shaft, plus spalling, with some pitting, and slight abrasion were present in the outer race.
By detecting the issue at an early stage, the maintenance team was able to replace the bearing during scheduled downtime and without disrupting the production process. We can imagine the consequences if the issue was not detected at this stage and the bearing was allowed to continue operating: the metal fragments would certainly affect the motor shaft, which would then also need to be replaced; and the facility would have to face unplanned downtime. In such a situation we could be looking at a loss of around 250K GBP.
By using the proper technology, with the proper maintenance procedures in place, the team was able to identify and solve the issue before it became a major problem. This case study shows how powerful ultrasound technology can be, especially when used in sensors connected to the network to provide truly online and permanent monitoring solutions.
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