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In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.

For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.

According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.

The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.

This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

This paper aims to provide an effective method so that the ultrasonic technique can be applied to the online debris particle detection. It proposes utilizing the waveshape features in discriminating the debris particle in lubricant.

The finite element model has been developed to investigate the scattering of the ultrasonic waves in lubricant containing single scatterer, such as the debris particle and the air bubble. The simulation results show that the results verify that different scatterers differ in the waveshape features. The static experiments were carried out on a specially fixture. The single spherical debris, long debris and air bubble were measured. The fast Fourier transform (FFT) method was applied to the analysis of the echo signals to obtain the features implicated in the waveshape.

The research of this paper verifies that different scatterers differ both in their shape features and in the FFT analysis features.

The rapid movement of the debris particles as well as the lubricant temperature may influence the measuring signals. Besides, the measuring signals are usually corrupted by noise, especially for the tiny debris. Therefore, researchers are encouraged to solve those problems further.

The paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

The paper provides a promising way that the ultrasonic waveshape features can be utilized to the identify debris particle online.

The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.

A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.

It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.

There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).

This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.

The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color.

It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis.

The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron.

Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

The purpose of this paper is to identify smaller wear particles and improve the calculation speed, identify more abrasive particles and promote industrial applications.

This paper studies a new intelligent recognition method for equipment wear debris based on the YOLO V5S model released in June 2020. Nearly 800 ferrography pictures, 23 types of wear debris, about 5,000 wear debris were used to train and test the model. The new lightweight approach of wear debris recognition can be implemented in rapidly and automatically and also provide for the recognition of wear debris in the field of online wear monitoring.

An intelligent recognition method of wear debris in ferrography image based on the YOLO V5S model was designed. After the training, the GIoU values of the model converged steadily at about 0.02. The overall precision rate and recall rate reached 0.4 and 0.5, respectively. The overall MAP value of each type of wear debris was 40.5, which was close to the official recognition level of YOLO V5S in the MS COCO competition. The practicality of the model was approved. The intelligent recognition method of wear debris based on the YOLO V5S model can effectively reduce the sensitivity of wear debris size. It also has a good recognition effect on wear debris in different sizes and different scales. Compared with YOLOV. YOLOV, Mask R-CNN and other algorithms%2C, the intelligent recognition method based on the YOLO V5S model, have shown their own advantages in terms of the recognition effect of wear debris%2C the operation speed and the size of weight files. It also provides a new function for implementing accurate recognition of wear debris images collected by online and independent ferrography analysis devices.

To the best of the authors’ knowledge, the intelligent identification of wear debris based on the YOLO V5S network is proposed for the first time, and a large number of wear debris images are verified and applied.

The presence of ferrous wear debris in lubricating oil may cause progressive damage in the internal combustion engines. Online monitoring of the size and concentration of these particles in the oil is a way to optimize the engine performance and its life cycle.

In this study, an online sensor was designed and fabricated to identify ferrous wear particles in the engine oil based on the induction method. The diameter of the sensor outlet duct was designed as small as possible to generate a high-intensity magnetic induction and achieve a proper sensitivity in the sensor. The experiments were designed and performed in offline mode. Furthermore, to evaluate the actual performance of the sensor in presence of iron particles in the oil, online tests were performed at different sizes and concentrations.

It was concluded from offline tests that the highest sensitivity of the sensor occurs at the frequency and voltage of 2.5 kHz and 120 V, respectively. According to the results of the online tests, the larger the particle size, the higher the peaks at the sensor output. Also, a high density of the peaks was observed in the sensor output graphs as the concentration of particles was increased.

The proposed sensor was able to identify ferrous wear particles larger than 125 µm separately, which is the failure limit in the internal combustion engines.

This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors.

The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure.

Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure.

Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.

The purpose of this study was to perform a complete experimental assessment of a family of oil ferrous wear debris sensor is performed. The family comprised the original sensor and its re-engineered evolution, which is capable of detecting both amount and size of wear debris particles trapped by the sensor and some predefined oil condition properties.

In this work, the first step was to perform a design of experiments for the sensor validation. A specially defined test rig was implemented, and different ferrous wear debris was collected. For each sensor, two different tests were performed. The first test was called a “void test”, where quantified amounts of debris were collided with the sensor without oil. The second one was a dynamic test, where the sensor was installed in the test rig and different amounts of wear debris were added at a constant rate. In addition, specific tests related with oil properties detection were studied.

The results show excellent correlation of the sensor output signal with the amount of wear debris and a satisfactory detection of debris size in all ranges. Also, the dynamic test presented adequate representativeness, and sensors performed well in this scenario.

This paper shows the practical implementation of this type of sensor and the usual detection range and rate of detection for different debris size and quantities.

This work has a great utility for maintenance managers and equipment designers to fully understand the potential of this type of sensor and its suitability for the application required.

The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment.

A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included.

Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition.

The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.