What is acoustic analysis?
Acoustic Analysis (AA) uses advanced microphones to measure the sounds and frequencies from equipment and analyze the actual equipment emissions vs. known failure frequencies to diagnose potential failures
Acoustic analysis provides a way of understanding equipment and preventing failures that doesn’t rely on typical predictive maintenance (PdM) metrics, like vibration or heat. This makes it an important and flexible part of a PdM program’s toolkit.
Identifying failure conditions with acoustic analysis helps save money by:
- Predicting failures before they happen, increasing machine reliability
- Reducing the overall amount of downtime
- Saving money on equipment costs due to proper maintenance
- Scheduling optimal maintenance work by predicting failur
Types of acoustic analysis tools
Like other PdM tools, acoustic analysis is now far more advanced than its bulky predecessors, integrating with Internet of Things (IoT)-connected technology and mobile devices.
Most often, acoustic analysis is performed by using specialized microphones that are placed next to critical assets. These microphones measure the level and frequency of sounds, which are then output to an adaptive learning algorithm for analysis
In particular, acoustic analysis is unique because of its ability to distinguish between multiple (sometimes dozens or hundreds) of different signals to isolate problematic sounds.
There are two primary methods of gathering acoustic data:
- Sonic: Sonic microphones gather sound data on the plane of sounds that humans can hear. Because the range of sound measured is far lower than ultrasonic, sonic acoustic analysis is somewhat less flexible. Sonic microphones typically gather sound data from low- and high-rotating machinery, making it useful for understanding the lubrication levels of equipment.
- Ultrasonic: On the other hand, ultrasonic microphones gather sound at a frequency higher and lower than human beings can hear. Ultrasonic microphones gather valuable data on machine stress and friction, making it a versatile tool for mechanical as well as electrical equipment.
As well as equipment, there are two widely-used analysis models:
Structure-borne analysis: In a structure-borne analysis system, microphones and sensors must be directly touching the equipment they are measuring. The microphone can then detect sounds and signals that move through the structure to predict fault.
Airborne analysis: Like its name implies, airborne analysis works by measuring the sound signals radiated from assets without needing to touch the actual asset itself. This provides a great deal more flexibility in regard to sensor setup, while providing an additional challenge in regard to filtering out background noise.
Which type of acoustic analysis tools to use depends entirely on the facility and equipment being measured. However, ultrasonic microphones see more and more use as time goes on because of their greater versatility and ability to distinguish between many similar sounds to find potential failures.
How to use acoustic analysis for predictive maintenance
Like other predictive maintenance tools, acoustic analysis aims to prevent failures from occurring by constantly monitoring the condition of equipment. It does this by gathering sound data (whether that’s on the human spectrum or not) and analyzing the sound against known failure frequencies.
In a typical environment, gears might make a very loud grinding noise if they are not lubricated enough. Interestingly, though, much of the “sound” that equipment makes isn’t actually hearable to the average maintenance technician. This is called “noiseless sound,” and it refers to the stress and friction a machine undergoes as it works.
It is by measuring this stress that acoustic analysis can improve preventive maintenance efforts.
Acoustic analysis can be used to determine the following:
- Lubrication analysis and training: Acoustic analysis can detect poor lubrication, leading to better overall lubrication measures and training for technicians.
- Machine stress: The stress and wear on a machine can be determined by listening to the frequency signals it emits. Using ultrasonic microphones, it’s even possible to isolate single signals out of a large number of sounds.
- Anomalous behavior: Sounds that are considered abnormal can be measured and used as detectors of irregular operation, leading to fixes prior to breakdowns.
Example of acoustic analysis
Take, for instance, a power station for a hydropower company. The station utilizes a massive amount of moving parts – this means that to accurately measure all conditions within the power station, the company would need to install a similarly massive number of sensors. Even within acoustic analysis, using a sonic microphone would be inefficient, as the amount of background noise would create unusable data.
However, with ultrasonic acoustic analysis, it’s possible install a small number of ultrasonic microphones that look for specific stress frequencies in various equipment. The acoustic algorithms calculate these frequencies against known failure frequencies, allowing maintenance technicians to perform maintenance before failures occur (which is important, considering the amount of power lost if the station stops functioning).