Can better ‘listening’ reveal mechanical faults earlier?

Every mechanical assembly produces acoustic emissions. Cranks, bearings, switches and far more make noise — the kind that can be heard and the kind that advanced technology can detect to determine the health of the mechanism. One area where this acoustic emission technology has not kept up, though, is for journal bearings: the support for rotating shafts in various machines, including transmission engines. Journal bearings depend on a film of oil surrounding the shaft to maintain operation. Should the oil become contaminated or some other mechanical failure occur, the entire mechanism can be at risk. Researchers have studied acoustic emissions for detecting mechanical failure since the 1950s and specifically for journal bearings since 1985. Now, an international team has pulled together the latest in the field to better understand how far it has come and what direction it should go.

 

“This review, published on Jan. 12 in Friction, aims to analyze the current research on fault diagnosis of sliding bearings via acoustic emission technology to guide a more effective health assessment of sliding bearings, offering valuable insights to promote the widespread application of acoustic emission technologies in the condition monitoring of rotating lubricated machinery,” said corresponding author Lingli Jiang, professor, School of Mechatronic Engineering and Automation, Foshan University.

 

Journal bearings are a type of sliding bearing, which can include any number of shapes like flat plates or other sliding pads. Journal bearings are a subset limited to cylindrical shafts that rotate.  

 

“Journal bearings often operate under heavy loads or at high rotational speeds for extended periods,” Jiang said. “As a result, friction and wear on journal bearings operating under such extreme conditions are inevitable and must be closely monitored.”

 

According to Jiang, the unique structure of journal bearings dictates that the condition of the lubricating oil film is crucial for determining lubrication regimes and evaluating the bearing’s early faults. The researchers noted that statistical data revealed faults related to the oil film account for up to 70% of journal bearing failures.

 

“When lubricant quality deteriorates due to contaminants, the film can partially rupture, and consequently, adhesive wear, abrasive wear, surface fatigue wear, and elastic or plastic deformation will occur step by step,” Jiang said. “Therefore, practical techniques are urgently needed to identify these faults.”

 

That’s where acoustic emission technology comes in. There are a number of existing assessment approaches to identify journal bearing faults, but many require access that active machines cannot provide, or are not sensitive enough to detect early faults, when they might still be mitigated before more catastrophic failure. Acoustic emission is specifically the strain energy released as elastic waves generated by the deformation or cracking of materials or other components, Jiang said. Acoustic emission signal waveforms can be classified into either burst signals or continuous signals.

 

“The former is sporadic and temporally separable, whereas the latter exhibits continuous variations over time in the case of persistent AE signals,” Jiang said.

 

The team assessed 140 studies that analyzed acoustic emission technology use for monitoring the condition of and diagnosing faults of journal bearings and found that its application remains relatively limited compared with its use with seals, gears, roller bearings and more.

“However, as a nondestructive detection method, the acoustic emission technique offers distinct advantages for evaluating the condition of sliding surfaces, especially in the early detection of rotor machinery failures,” Jiang said. “Consequently, this technology has gained increasing attention for monitoring journal bearings in recent years. Although significant progress has been achieved, there is a need for further development to address existing gaps and explore new directions.”

 

According to the researchers, future work should focus on better understanding the mechanism of acoustic emissions to diagnose early faults; clarifying signal data, which can be very noisy with blips not related to faults; exploring how to fuse data to better inform intelligent identification methods; establishing a database of failures in journal bearings for better comparison and identification; and refining acoustic emission modeling specifically for analyzing the mechanisms underlying faults.

Other contributors include Jiaojiao Ma, Jiefei Yu, Xianwen Zhou and Xuejun Li, School of Mechatronic Engineering and Automation, Foshan University, China; and Fengshou Gu, Centre for Efficiency and Performance Engineering, University of Huddersfield, United Kingdom.

The National Natural Science Foundation of China, the Guangdong Basic and Applied Basic Research Foundation and the Tribology Science Fund of the State Key Laboratory of Tribology in Advanced Equipment funded this research.

DOI Link:

https://doi.org/10.26599/FRICT.2025.9441080

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