1. Introduction
In today’s technological landscape, there is a growing demand for mechanical systems that can operate with high reliability. These systems are utilized in a diverse range of applications, including aerospace, automotive, energy, and agriculture industries. The importance of appropriate maintenance for industrial equipment increases day by day to avoid equipment failures and also prevent high costs and economic losses [
1]. The need for high-reliability mechanical systems has emerged as a result of the increasing complexity of technology and the need to ensure safe and reliable operation under various conditions and stresses. Engineers and technologists in different fields work collaboratively to design and develop high-reliability mechanical systems that incorporate redundancy and undergo rigorous testing and verification procedures to ensure their reliability and safety [
2]. Condition monitoring is a vital program for ensuring the safety, durability, and efficiency of machines. It involves the regular monitoring of a machine’s health through various techniques such as vibration analysis, oil analysis, and thermography. By tracking and analyzing the machine’s condition over time, potential issues and faults can be detected early, allowing for timely maintenance and repairs before significant damage occurs. This proactive approach to maintenance helps to minimize downtime, reduce repair costs, and extend the life of the machine. Condition monitoring is particularly critical in industries where machine failure can result in significant safety risks and financial losses, such as manufacturing, transportation, and energy production [
3,
4]. The lubricant in a machine function similarly to blood in a living organism, providing vital protection against wear and tear. Just as blood delivers oxygen and nutrients to vital organs, lubricant delivers essential oils and additives to the machine’s moving parts, reducing friction and preventing damage. Without proper lubrication, a machine’s performance will suffer, and it may experience premature failure. Therefore, regular lubrication maintenance, including oil changes and filter replacements, is necessary to ensure the smooth and efficient operation of the machine [
5,
6]. However, the primary purpose of a lubricant is to mitigate tribological operational issues associated with friction and wear [
7]. By analyzing lubricating oil, potential issues can be detected early, reducing the risk of costly repairs and downtime. Regular oil analysis is necessary to ensure safe and reliable system operation [
8].
Figure 1 illustrates the evolution of maintenance strategies over time, from reactive maintenance to more proactive approaches such as preventive and condition-based maintenance [
9]. This evolution has significant implications for improving safety, reliability, and cost-effectiveness in industries such as manufacturing, transportation, agriculture, and energy production. There are various methods for evaluating lubricating oil, as outlined in [
10]. These methods include physical and chemical techniques such as chromatography, spectral analysis techniques such as infrared absorption spectroscopy (IAS) and Raman spectroscopy, and electrical diagnosis methods such as return voltage measurement and frequency-domain spectroscopy. Each of these techniques has its advantages and limitations and is useful for different applications.
Determining the appropriate timeline for lubricant maintenance is a controversial issue. One investigation undertook a detailed examination of sophisticated material characterization techniques, such as atomic force microscopy, thermal analysis, and X-ray diffraction. Particular focus was directed toward appraising the applicability of graphene as an emerging nanomaterial to heighten the performance characteristics of several materials employed in pavement engineering, including asphalt binders [
11]. While laboratory methods can provide precise results on lubricant condition, they may not be sufficient in determining the optimal maintenance timeline, as it depends on various factors such as the machine’s operating environment and duty cycle. A common limitation of laboratory methodologies is their limited ability to fully represent the state of oil at a particular point in time and their inability to account for changes in its state over time [
12]. An effective lubricant maintenance program should consider factors such as the machine’s history, manufacturer’s recommendations, and best industry practices. Valuable data about an engine’s operational circumstances and maintenance history can be obtained from the machinery’s history, aiding in the identification of optimal maintenance schedules. Additionally, manufacturers’ guidelines can provide direction on the suitable lubricant type and change intervals for the engine [
13]. Regular oil analysis and consultation with lubricant experts can help determine the optimal maintenance timeline [
14,
15]. Field testing offers advantages over laboratory testing in determining lubricating oil maintenance schedules. The primary advantage is that field testing provides a more accurate representation of the oil’s state under real operational conditions [
16]. Electrical techniques provide an easy-to-use and cost-effective alternative to laboratory methods for evaluating lubricant condition. They allow for in situ measurements that enable early identification of potential issues, reducing the risk of downtime or significant damage. The emergence of electrical techniques has expanded the range of options available for lubricant evaluation [
17,
18]. For instance, systems such as the “LUBSTER” system use InfraRed and color pigment sensors to indicate the condition of the oil in the dashboard system [
19]. This helps to improve the efficiency of engines and machinery that rely on lubricating oil. Electronic technology provides accurate and reliable data on the condition of lubricating oil. For example, one study used multi-sensor information fusion technology to monitor the quality of automotive engine lubricating oil. The study evaluated the moisture content of lubricating oil, dielectric constant, scatter degree and transparency of infrared light, and iron-grinding particle content on the permeability and ultrasonic reflectivity as input factors and established a monitoring model of lubricating oil quality using the theory of information fusion technology. The results showed that the lubricating oil quality monitoring model of multi-sensor information fusion technology can more accurately reflect the quality of lubricating oil [
20]. One example is the development of smart sensor systems for monitoring the operational condition of in-service diesel engine oils. These sensors can provide real-time condition monitoring and project the remaining usable life of the lubricant, reducing or eliminating the need for traditional oil analysis methods [
21]. Electronic technology can help to reduce the environmental impact of used lubricating oil. For instance, one study explored the potential of using differences in wear particle kinematic characteristics to recognize changes in wear particle diameter and oil viscosity. The study designed and fabricated a wear particle kinematic analysis system (WKAS) that was applied to a pin-disc tester, and the experimental results showed that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity [
22]. By monitoring the quality of lubricating oil, potential problems can be detected early, preventing oil contamination and reducing the environmental impact of used lubricating oil. Intelligent diagnosis technology can provide a real-time and continuous collection of data during the operation of the diesel engine, which can help in detecting faults and diagnosing problems in a timely manner [
23]. Soft computing methods have also been employed for lubricant condition analysis, eliminating the need for expert intervention. These methods use artificial intelligence and machine learning algorithms to analyze data from sensors and determine the condition of the lubricant. Soft computing methods can help to detect subtle changes in lubricant condition that may not be apparent through traditional methods, improving the accuracy and reliability of the evaluation. Moreover, these methods can be integrated into the machine’s control system, providing real-time monitoring and alerts for maintenance professionals. Soft computing methods offer a promising solution for improving lubricant maintenance, reducing the risk of downtime and costly repairs [
15,
24,
25]. Sophisticated technologies that are employed in monitoring diesel engines encompass deep transfer learning and genetic algorithms. For example, an investigation of a diagnostic approach has been proposed that leverages intelligent methodologies, such as optimized variational mode decomposition and deep transfer learning, to address fault diagnosis in diesel engines [
26]. Furthermore, a diagnostic framework for marine diesel engines, founded on an adaptive genetic algorithm, has been devised to achieve efficient and precise classification of faults occurring in diesel engines [
27].
Lubricant condition monitoring is an area of active research, with soft computing techniques emerging as a promising focus. These techniques use artificial intelligence and machine learning to improve the accuracy and efficiency of lubricant maintenance, reducing the risk of downtime and costly repairs [
15,
28,
29,
30,
31]. Overall, soft computing techniques offer a faster [
32], more accurate [
33], and more adaptable [
34] approach to diesel engine lubricant monitoring than traditional methods. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce operation costs by predictive maintenance without interrupting normal machine operations [
35]. These techniques are rather easy to develop and perform and can help with accurate means of condition assessment and fault diagnosis. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues, and this motivates the examination of online condition monitoring [
36]. The accuracy of machine learning models in predicting engine lubricant properties has been investigated in recent studies. For example, one study conducted a preliminary test using K-nearest neighbor (KNN) and Radial Basis Function (RBF) models for engine lubricant spectral analysis. The study found that the models reduced twelve indexes to seven, including iron, chromium, lead, copper, aluminum, nickel, and TDPQ. The RBF-ANN modeling approach was particularly noteworthy for its accuracy in detecting all three sizes of the training sets, with an impressive accuracy rate of approximately 99.85%. These results suggest that machine learning models have great potential for improving engine lubricant analysis and predicting properties with high precision [
15]. Another study explored the use of the Recursive Feature Elimination (RFE) method to predict external wear failure by reducing independent variables. The study found that this approach achieved an impressive accuracy of 94.20%. Interestingly, the study also revealed that the presence of iron, aluminum, and lead were particularly important in assessing wear conditions. These findings demonstrate the potential of machine learning models to identify important factors that contribute to engine lubricant properties and failure prediction [
30].
Dielectric or Impedance Spectroscopy (IS) is a powerful and versatile technique for measuring the electrical properties of various materials, including concrete, paper, liquids, and even biofuels. This non-destructive method is not only cost-effective but also highly accurate, providing researchers and engineers with valuable insights into the electrical behavior of these materials [
37]. One of the main advantages of these methods is that they are non-destructive, meaning that they do not damage the material being tested. This makes them ideal for testing delicate or expensive materials, as well as for testing materials that cannot be easily replaced or repaired [
38]. Additionally, these methods are cost-effective, as they do not require expensive equipment or specialized training to perform [
39]. Another advantage of dielectric or impedance methods is that they provide high-precision measurement results. These methods are able to detect small changes in the electrical properties of materials, which can be used to identify subtle differences between materials or to track changes over time [
40]. Researchers have proposed a novel approach to locomotive system maintenance by investigating the relationship between dielectric properties and metallic and non-metallic particles found in engine oil. In this study, artificial neural networks were employed to determine the correlation between the dielectric constant and oil impurities, as well as the dielectric loss factor and oil impurities. Specifically, the researchers used elemental spectroscopy as inputs and dielectric properties as outputs in their modeling approach. Impressively, the study achieved highly promising regression values, with the dielectric constant achieving an R value of 0.8513 and the dielectric loss factor achieving an R value of 0.8015 at 7.4 GHz. These results demonstrate the potential of machine learning models to accurately predict engine lubricant properties and aid in effective maintenance strategies [
15].
Table 1 provides a comprehensive summary of recent studies that have utilized soft computing tools for engine and component condition evaluation in machinery. This table highlights the growing interest in machine learning techniques for predicting engine lubricant properties and identifying potential issues before they become major problems. It is evident that the field of engine and lubricant research is vast, with a plethora of studies that have investigated diverse methods for enhancing performance and curtailing energy loss in internal combustion piston engines. These techniques encompass the use of lubrication quality [
41], coatings [
42], and multifarious tribological approaches [
43]. Consequently, there exist multiple prospects for tackling lubricant and engine health concerns, and this academic inquiry delves into maintenance and management techniques that leverage the electrical properties of lubricants and neural networks.
The research conducted in this study brings forth a novel approach to predicting the elemental spectroscopy of lubricants based on their electrical properties. The main objective of this study was to compare the performance of various soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of lubricants based on their electrical properties (ε′, ε″, and tan δ). While previous research has primarily focused on a limited number of engine lubricant pollutants, this study goes beyond by considering the effects of multiple elements including Fe, Pb, Cu, Cr, Al, Si, and Zn. This comprehensive analysis of various elements in lubricants is a unique aspect of this research. Another notable novelty of this study is the source of lubricant samples. Unlike previous studies that primarily used laboratory-prepared samples, this research examines lubricant samples extracted directly from the engine. This enables the study to closely mimic real-world conditions, enhancing the accuracy and applicability of the findings. The elements present in the lubricant were identified using spectroscopy, and the electrical properties of each sample were subsequently measured. Furthermore, soft computing algorithms were employed to explore the complex relationship between polluting elements and the electrical properties of lubricants. These algorithms leverage data from both existing literature and experiments conducted within this study, resulting in a comprehensive analysis of the subject matter. The importance of these findings should not be underestimated. The findings of this study have important implications for the development of effective monitoring conditions for engine lubricants. Moreover, the potential for online and portable methods to detect and diagnose these faults is also promoted through this research. In the subsequent section, a detailed description of the research process will be presented, highlighting the key results obtained from this groundbreaking study.
4. Conclusions
This study aimed to evaluate the performance of soft computing models in predicting engine lubricant elemental spectroscopy based on their electrical properties. A dataset of 49 lubricant samples was used to train and test various soft computing models (RBF, MLP, ANFIS, GPR, and SVM), and their performance was assessed using error metrics (RMSE, MAPE, and EF). The preliminary statistical analysis using correlation analysis revealed various relationships between elemental spectroscopy and electrical properties. Fe showed a weak inverse relationship, while Pb, Cu, and Cr had a direct relationship with electrical properties. Al and Si also displayed an inverse relationship, and Zn showed a strong inverse relationship with electrical properties (
Table 4 depicts the correlation coefficients). The performance evaluation of the soft computing models showed that the RBF and ANFIS models consistently outperformed the other models for Fe, Cu, Si, and Zn at different frequencies. MLP, SVM, and GPR models had a poorer performance for these elements. For Pb, Cr, and Al, where MAPE could not be calculated, the RMSE values indicated that RBF and ANFIS models made smaller errors in predicting the concentrations of these elements. The frequency factor also influenced the predictive performance of the models, with higher frequencies generally leading to more accurate predictions for certain elements (
Table 5 reports RMSE and MAPE). The results demonstrated that the radial Basis Function (RBF) model consistently outperformed the other models, achieving the most accurate predictions, especially at the highest frequency of 7.4 GHz. Our findings demonstrate that the RBF model can accurately predict engine lubricant properties, including Fe, Pb, Cu, Cr, Al, Si, and Zn, with high precision. Specifically, the RMSE values obtained for these elements were 0.9, 0.3, 2.2, 0.2, 0.1, 0.4, and 1.0, respectively. These low RMSE values indicate that the RBF model can effectively capture the complex relationships between the electrical properties and elemental concentrations in engine lubricants. This indicates a strong correlative and predictive relationship between lubricant electrical properties (ε′, ε″, and tan δ) and elemental spectroscopy. Tuning the RBF parameters, including increasing the hidden size and optimizing the training algorithm, further improved its performance. This shows that fine-tuning machine learning models can improve their accuracy and reliability for practical applications. Sensitivity analyses were carried out to determine the most influential input variables, which revealed that the combination of ε′, ε″, and tan δ yielded the lowest Root Mean Square Error (RMSE) values for all elements. Notably, the removal of ε′ led to a significant increase in RMSE, emphasizing its crucial role in predicting engine lubricant properties. These findings suggest that lubricant monitoring programs based on measurements of electrical properties, using machine learning models such as RBF, may effectively detect machine faults early through the prediction of elemental spectroscopy changes. Despite these limitations, this approach demonstrated in the current study could provide insights for developing effective condition-based maintenance strategies utilizing real-time lubricant analysis. Such systems could detect abnormal changes early and project remaining lubricant life. These results help advance understanding of the correlation between lubricant electrical properties and elemental spectroscopy and highlight the potential of soft computing methods for real-world lubricant monitoring applications. However, further research is needed to test the approach on a wider range of lubricant types and operating conditions and to explore different machine-learning models and optimization techniques. Collaborating with institutions from other countries presents valuable opportunities to access diverse datasets, different measurement techniques, and a wider range of samples. To enhance our modeling further, we propose exploring ensemble methods such as random forests or gradient boosting, which have been known to improve performance and generalization. In addition, to capture temporal dependencies in engine lubricant properties, we recommend utilizing deep learning models such as recurrent neural networks (RNNs). Optimization techniques such as hyperparameter tuning through grid search, randomized search, or Bayesian optimization, as well as feature selection methods, can also be employed to improve model performance. Nonetheless, the current study provides a solid foundation and starting point for future work in this area. Also, this work provides a practical framework for accurately predicting lubricant conditions based on electrical measurements.