33 Analytical Instrumentation
Although integrated sensors may offer a more effi cient method of LCM compared to traditional oil sampling and analysis, long-term testing is still required to assess the sensor’s capability to detect chemical reactions with contaminants over extended periods.
4. Vibration Analysis
An indirect way of LCM is through vibration analysis, which monitors changes in a system’s performance by tracking its established vibration signature16
. When a certain vibration signature
. Ninety percent of the problems detected by vibration analytics are related to balance and alignment, which causes excessive vibration and eventually wear down bearing surfaces if not addressed18
. Thus, unusual variations in amplitude or frequency usually indicate imminent
machine failure –identifying these variations allows for repairs before signifi cant malfunctions occur.
Zamorano et al. at the Universidad Carlos III and the Universidad de La Laguna examined the use of vibration analysis on centrifuge lube oil separation systems in marine vehicles19
. After
preprocessing the vibration data to remove noise, normalizing it, and extracting relevant features, the signals were decomposed into time-frequency components using wavelet packet transform, allowing for the analysis of different frequency bands. The energy levels of the motor were measured twice: the fi rst measurement was taken right after a maintenance task, and the second measurement was taken after several working hours. Figure 5 shows that the motor energy increased with each harmonic in the fi rst measurement but decreased at the second harmonic in the second measurement19
. This suggests that there is a possible correlation between changes Figure 2: Robustness validation of the sensor via a shaker for random vibration (top) and for shock tests (bottom)12
While the researchers believe that the sensors will not require additional maintenance beyond regular vehicle maintenance, they have yet to conduct long-term studies on the sensors’ reliability in prolonged stressful conditions. This could be done by increasing the parameters in stressful condition experiments, accelerating the aging process via UV radiation or oxidation, or by conducting virtual simulations. Once possible tests have been undertaken, on-the-fi eld performance results will dictate whether the robustness of the sensors in experiments will carry over to real applications. Furthermore, outdoor operating conditions for railways in varying climates may affect sensor performance, particularly in regions with extreme temperatures or heavy rainfall.
Hydraulic systems, on the other hand, typically use oil as a low-viscosity lubricant to operate at high speeds. Researchers at Dongguk University and Hyundai in Korea developed an integrated oil sensor to monitor hydraulic oil contamination in construction machinery13
. The sensor measured
absolute viscosity, density, temperature, and the dielectric constant of the lubricant to establish a benchmark for the effects of various pollutants found in construction environments. Figure 3 shows that when small amounts of dust or improper oil were introduced, the lubricant’s absolute viscosity and dielectric constant did not change signifi cantly13
. However, the introduction of
moisture resulted in measurable variations, particularly in the dielectric constant; more noticeable effects were observed with the presence of varnish–an insoluble deposit formed from oil oxidation14
. The dielectric constant refl ects the lubricant’s ability to transmit electric currents, which can be affected by contaminants or additives in the lubricant15 . Thus, this oil sensor is useful
for identifying contaminants that affect the lubricant’s dipole moment, but it is limited in its ability to detect other types of contaminants.
Figure 4: Energy variations between fi rst and second measurements of marine oil separation systems19 5. Soft Computing Methods
The widespread use of technology and artifi cial intelligence (AI) has already signifi cantly improved convenience and accuracy in both personal and business settings. Although still in development, AI can search databases far more quickly than humans, which could be especially useful for new staff with less experience. It also has the potential to perform around-the-clock monitoring if proven reliable against bias and complex situations. Monitoring might function similarly to AI in home security systems, which learns the homeowner’s patterns and sends out alerts if it detects any threats or potential risks. Specifi cally, soft computing – referring to a branch of AI that relies on learning from data to make “human-like” decisions, focusing on approximate solutions and models while tolerating imprecision, variations. and noise19
– presents a promising method for LCM.
Pourramezan et al. at the Ferdowsi University of Mashhad and the Tarbiat Modares University in Iran utilized soft computing methods like K-Nearest Neighbor (KNN) and Radial Basis Function Artifi cial Neural Networks (RBF-ANN) to predict lubricant condition and engine health using parameters like viscosity, acidity, particle count, and vibration data21
. The software was programmed to diagnose
engine health conditions in three categories (normal, caution, or critical) based on seven key lubricant indices. After training all models on historical datasets, they found that engine health diagnosis accuracy across all models was at least 97%, with larger training datasets resulting in higher accuracies. Figure 6 depicts the results of the KNN method, which saw the overall lowest accuracy of greater than 96% for lubricant wear when trained on 40% datasets21
in vibration patterns and lubricant health, as lubricants degrade with continued system operation. Being able to detect variations in energy levels from vibration data allows for the comparison of patterns and individual values at specifi c harmonics to reference values, but the relationship between lubricant degradation and change in vibration data must be further studied and confi rmed.
is associated with normal operation, the deterioration of lubricants essential for machine health will be associated with a change in the vibration signature. Thus, vibration analysis could aid LCM by providing a warning for the presence of degraded lubricants, especially if certain frequencies can be linked to defects in specifi c locations. The vibration spectrum of a rotating equipment will show peaks at multiples of the fundamental rotational frequency, called harmonics. For instance, a motor running at 1500RPM exhibits synchronous peaks at 1500RPM, 3000RPM, and so on17
. Despite the lower
accuracy with smaller training sets, soft computing models demonstrated impressive reliability, with potential for further improvement as more data becomes available.
Table 2: KNN method for detecting lubricant wear using three sizes of training sets21
Figure 3: Variations in hydraulic oil properties – absolute viscosity (top) and dielectric constant (bottom) – when introducing dust (left) and more than 3000ppm of moisture (right)13
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