Tie Bearing Defects To ESG Goals

Fig. 1: Bearing has inner and outer race false brinelling and ball defect in Stage 1 failure.

By Howard W. Penrose, PhD, CMRP, President, MotorDoc LLC

One of the challenges with most reliability programs is tying maintenance activities to actual ESG (environmental, social, and governance) goals using measured data. As an example, consider what a bearing defect curve looks like as we progress from a Stage 1 through a Stage 3 degradation. We’ll review two sets of data and then an average from an electrical signature analysis failure library and apply the resulting curve to an example.

In the first case study, a larger machine is operating at 686-kW load (input) and 610-kW load (shaft output at 96% efficiency) with a drive-end bearing with slight false brinnelling on the inner and outer race and poor lubrication, causing some damage to the ball (Figure 1). The total individual losses by defect (kilowatt spectra), as measured with electrical signature analysis, before replacing the bearing showed direct losses from these defects totaling 2.4 kW. This represents 0.4% of the total load, an impact on electric machine efficiency of 0.1%. At 6,000 hours of operation, average, and assuming static condition and load, this would represent 14,400 kWh and additional emissions of 10 metric tonnes of CO₂ over the course of a year. This case represents the type of defect found in a Stage 1 bearing failure and does not include the misalignment and other defects associated with this application.

In an average Stage 2 bearing failure, as found in a 600-hp motor operating at 126.7 kW (input) and 115.8 kW (shaft output) at 93.5% efficiency, losses across bearing defects as above were 1.2 kW. This represents 1% efficiency loss, or 10 times what was in the Stage 1 example. At 1.2 kW, the usage would be 7,200 kWh and emissions would be 5 metric tonnes CO₂ over 6,000 hours. To project further, based on an average from our database of ESA defect findings, we find that Stage 3 bearing failures jump to an impact of an average of 3%+ reduction in efficiency.

Fig. 2: This average losses curve is based on 30-kW electric motor bearing defect progression from Stage 1 through Stage 3.

If we take an example of a 50-hp motor using 30 kW and a progressing bearing fault similar to the two examples above, with 0.1% reduction for the first 2,000 hours, 1% for the second 2,000 hours, and 3% for the third 2,000 hours, we observe 60 kWh, 0.04 tonnes CO₂ initially, 600 kWh and 0.4 tonnes CO₂ as the fault progresses to Stage 2, and 1,800 kWh and 1.3 tonnes CO₂ at Stage 3. While this would total 2,460 kWh and 1.74 tonnes CO₂, it does not include other conditions such as lubrication degradation, the condition of the other bearing, or other driving factors for the bearing defect, including misalignment or belt tension. The curve for degradation may be shorter, but generally follows the same tendency that moves closer to a natural log curve as we approach Stage 4 bearing failures (steeper).

While the above values are conservative and represent the lower boundary of the curve associated with this type of failure, it should identify that, when defects are not addressed, the losses, increased operating costs, and emissions associated with defects can be significant. Monitoring and reporting this aspect of your maintenance program can tie your reliability program to corporate ESG goals. EP

Howard W. Penrose, PhD, CMRP, is president of MotorDoc LLC, Lombard, IL (motordoc.com). He chairs the wind-power standards and government relations participation for American Clean Power (ACP/AWEA), holds various IEEE standards positions, and is a past chair of SMRP. Reach him at info@motordoc.com.