Wear Debris Monitoring Research Articles

A Multi-Contaminants Detection Sensor Based on Digital Lock-In Amplifier Module With High Sensitivity and High Detectability

Wear debris monitoring in lubrication oil has emerged as a promising solution for health monitoring and fault diagnosis of mechanical equipment. However, the debris feature signal is weak and overwhelmed in the noise. Furthermore, the traditional inductive detection sensors suffer from poor detectability to distinguish non-ferromagnetic metal particles. To address these problems, in this paper, a triple-coil inductive debris sensor combined with a digital lock-in amplifier (DLIA) module was proposed to detect and distinguish multiple contaminants in hydraulic oil so as to achieve high sensitivity and high detectability. First, we introduced the sensor design and fabrication. Second, the triple-coil inductive debris sensor model, the DLIA principle, and the performance analysis of suppressing noise for lock-in amplifier were provided, respectively. Finally, both simulation and experimental platforms were built to substantiate the effectiveness of the proposed approach. Experimental results showed that the detection limit of the designed sensor for iron, copper, and aluminium particles could be inferred as 85.4 μm, 102.4 μm, and 111.6 μm, respectively.

A comparative investigation on wear characteristics of polymer and biopolymer gears

Abstract Plastic gear applications are rapidly increasing in transmission systems in automotive, domestic and kitchen machineries. Biopolymer gears are now being preferred over polymer gears due to its biodegradable properties. Hence, a comparative investigation of wear characteristics between polymer and biopolymer gears is studied. Injection molded biopolymer, PLA (polylactic acid) gear and polymer, ABS (acrylonitrile butadiene styrene) gear have been fabricated with appropriate properties. Gears are experimentally tested for the period of 3 h at three different rotational speeds (500, 1000 & 1500 rpm) with and without load (1 kg). Wear debris, material loss, thermal damage and microstructure surface condition monitoring have been investigated. PLA presents better adhesive wear resistance and shows thermal deformation and tooth breakage. Biopolymer gives better thermal and wear resistance than polymer gear. At low rotational speed of 500 rpm under no-loading condition, both PLA and ABS show higher weight loss and wear rate due to thermal damage occurred due to gear pump effect. Whereas when load is applied, PLA shows wear loss at all rotational speeds due to inferior thermal conductivity and ABS shows wear loss at high rotational speeds of 1000 rpm or 1500 rpm due to loading effect.

Online Symmetric Magnetic Excitation Monitoring Sensor for Metal Wear Debris

The metal wear debris in mechanical transmission can feedback the characteristics of mechanical equipment failure, and its online monitoring efficacy directly affects the operating performance of mechanical equipment. Specific to the need for online fault detection of the mechanical transmission system, a double-coil reverse excitation method is adopted to provide a magnetic field environment for metal wear debris testing in the lubricating oil circuit of the mechanical transmission system. Based on the magnetic field distribution model of a single-coil solenoids, a coupling magnetic field distribution model of reverse-symmetric double-coil magnetic excitation is established. On the basis of detecting the coils to obtain the signals of magnetic field disturbance when metal water debris passing through the magnetic excitation field, an online monitoring sensor for metal wear debris with a reverse-symmetric double-coil magnetic excitation balance magnetic field is developed. Taking the monitoring of metal wear debris in a large-aperture flow channel as an example, a semi-physical simulation test has been carried out on metal wear debris in different grain diameters and textures, whose experimental results indicate that this sensor can detect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 ~\mu \text{m}$ </tex-math></inline-formula> ferromagnetic metal and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1000 ~\mu \text{m}$ </tex-math></inline-formula> non-ferromagnetic metal in an 8 mm flow channel, which, as a detection sensor for metal wear debris, can also be used for metal wear debris monitoring in the mechanical lubricants of mechanical transmission and engines.

Deterioration state diagnosis and wear evolution evaluation of planetary gearbox using vibration and wear debris analysis

The deterioration state of planetary gearboxes with pitting faults cannot be diagnosed and evaluated effectively with a single type of monitoring signal and traditional features. In this study, a new diagnose method was proposed based on the tribology and vibration signal of planetary gear systems. A new feature extraction approach was exploited to construct a multi-feature fusion relevance vector machine (MFFRVM) model for diagnosing the deterioration state of planetary gearboxes. Meanwhile, a novel index for online wear debris monitoring (ADIDC) was extracted to evaluate the wear evolution of planetary gears. Condition monitoring experiments were conducted on a planetary gearbox. Experimental results show that the diagnostic method combining the MFFRVM model and ADIDC index achieves better accuracy than other methods. This study provides guidance for the wear evolution evaluation of planetary gears with pitting faults and the early fault recognition of planetary gearboxes.

A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil.

Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network.

Self-powered artificial joint wear debris sensor based on triboelectric nanogenerator

Replacing the diseased joints with artificial prostheses has long been a common medical mean to alleviate pain or disability, in which the wear debris-induced aseptic loosening is the main mechanism for failure of artificial joint replacement. Timely and in situ detection of the wear debris is necessary to prevent further deterioration of the joint, and provides a basis for the lifetime diagnosis of artificial joint. Here, a self-powered sensor based on triboelectric nanogenerator (TENG) was developed for detecting wear debris generated in artificial joint. The TENG with polyethylene film and steel ball was designed into artificial joint configuration and fabricated by thermo-compression, which could in-situ detect the production of wear debris. Furthermore, the dependence of electrical output signals on the amount and size of the debris was investigated, which demonstrated obvious decrease in voltage with the increase in the amount and size of the particles. Finally, the fabricated sensor was tested in joint wear simulator, which achieved the real-time wear debris monitoring. This work expands the application of TENG in artificial joint wear detection, which is expected to promote the development of biomedical sensor and intelligent healthcare.

A Novel Impedance Micro-Sensor for Metal Debris Monitoring of Hydraulic Oil

Hydraulic oil is the key medium for the normal operation of hydraulic machinery, which carries various wear debris. The information reflected by the wear debris can be used to predict the early failure of equipment and achieve predictive maintenance. In order to realize the real-time condition monitoring of hydraulic oil, an impedance debris sensor that can detect inductance and resistance parameters is designed and studied in this paper. The material and size of wear debris can be discriminated based on inductance-resistance detection method. Silicon steel strips and two rectangular channels are designed in the sensor. The silicon steel strips are used to enhance the magnetic field strength, and the double rectangular detection channels can make full use of the magnetic field distribution region, thereby improving the detection sensitivity and throughput of the sensor. The comparison experiment shows that the coils in series are more suitable for the monitoring of wear debris. By comparing and analyzing the direction and the presence or absence of the signal pulses, the debris sensor can detect and distinguish 46 µm iron particles and 110 µm copper particles. This impedance detection method provides a new technical support for the high-precision distinguishing measurement of metal debris. The sensor can not only be used for oil detection in the laboratory, but also can be made into portable oil detection device for machinery health monitoring.

Review on online inductive wear debris monitoring technology

Online oil monitoring technique is now an important development means to monitor wear condition and diagnose wear fault real time. Inductive wear debris monitoring is the research hotspot in the current online oil monitoring for its unique characteristics. Based on the current domestic and international online inductive wear debris monitoring, the relevant comments to the online inductive wear debris monitoring technology were given. Then, the positive and inverse problems of numerical analysis of inductive wear debris monitoring are analysed. Based on the above, the difficulties and shortages of online inductive wear debris monitoring are studied and the development trend and research focus of online inductive wear debris monitoring are discussion to provide for the oil online technology research.

Online wear characterisation of rolling element bearing using wear particle morphological features

Rolling element bearings are commonly used in rotatory machines and their operation conditions need to be monitored to prevent catastrophic failures. This research aims at developing an effective online monitoring method to analyse wear severity and wear mechanisms of bearings. For this purpose, morphological features of wear debris from bearings, including quantity, colour, size and shape, are extracted from videos of moving particles carried in lubrication oil. Moving particles are detected and tracked based on the Gaussian background mixture model and the blob detection algorithm, and the target particles are separated from the image background by background subtraction. Then particle shape and dimensional features both in two- and three-dimensions are extracted from multiple images captured from different views. At the same time, the numbers of moving particles are determined based on the tracking results. The developed techniques for online particle feature extraction are applied to a rolling element bearing test rig. The particle quantity is used to indicate the trend of wear state evolution, while the colour, size and shape features of wear debris are used to classify particles into different types for wear mechanism analysis. The relationship between wear mechanisms and the trend of wear states in the time domain is established. Based on the gathered information and wear analyses, a comprehensive characterisation is obtained for online monitoring the wear state evolution as well as evaluating the wear severity and wear modes. The proposed method is able to provide wear mechanism information for root cause analysis and thus enhance the capabilities of existing online wear debris monitoring techniques.

An online de-noising method for oil ultrasonic wear debris signal: fuzzy morphology component analysis

PurposeIn 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.Design/methodology/approachFor 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.FindingsAccording 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.Research limitations/implicationsThe 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.Practical implicationsThis paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.Originality valueThis 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.

Review on Condition Monitoring of Bearings using vibration analysis techniques.

A Bearing is one of the important components in the Rotary machines and has been widely used in various industries in many of the applications such as shaft mountings, to reduce friction as well as facilitate relative motion between the two components etc. It is therefore very essential to determine the early faults conditions from bearings. There are various methods to detect faults in the bearings, such as vibration monitoring, wear debris monitoring, temperature monitoring, soap techniques, non destructive test etc. Vibration signal analysis may be one of the commonly used techniques for checking the condition and finding faults in bearings. Vibration analysis has been used as a predictive maintenance procedure in the machine maintenance. By adopting appropriate signal processing techniques, changes in vibration signals due to faults can be detected to aid in maintaining the bearings health condition. By detecting and analyzing the machine vibration, it is possible to determine and predict the machine failure. Early fault detection of the bearings is possible by analyzing the vibration signal using different techniques. This paper give a relative of various techniques used for finding the fault in the bearings based on vibration analysis method.

Restoration of low-informative image for robust debris shape measurement in on-line wear debris monitoring

Abstract As a significant technique in machine condition monitoring, wear debris analysis enables investigation of machine running condition with respect to debris features including size, quantity and morphology. In particular, being capable of providing more comprehensive morphological information, three-dimensional debris features are regarded essential and often acquired through a video-based debris imaging process. However, debris images captured often suffer degradation due to debris motion blur and lubrication contamination, that hinder reliable debris features extraction. To address the image degradation issue, a new method of wear debris image restoration is developed to reduce the effect of blur. In order to avoid the expensive computation involved in blind deconvolution methods, the debris image was restored using localized boundary features. Based on the fact that debris area and background area indicate distinctive brightness, a step edge model is applied to describe the original debris boundary. Localized kernels on each side of debris are then determined. Next, restorations are conducted with the estimated kernels to produce sharper debris profiles with respect to different motion features. Final restoration is conducted by fusing the restored profiles according to the maximum local sharpness. Experimental results have demonstrated that this method allows reliable features extraction from blurred image, improving the robustness of video based wear debris analysis.

Monitoring of Non-Ferrous Wear Debris in Hydraulic Oil by Detecting the Equivalent Resistance of Inductive Sensors.

Wear debris in hydraulic oil contains important information on the operation of equipment, which is important for condition monitoring and fault diagnosis in mechanical equipment. A micro inductive sensor based on the inductive coulter principle is presented in this work. It consists of a straight micro-channel and a 3-D solenoid coil wound on the micro-channel. Instead of detecting the inductance change of the inductive sensor, the equivalent resistance change of the inductive sensor is detected for non-ferrous particle (copper particle) monitoring. The simulation results show that the resistance change rate caused by the presence of copper particles is greater than the inductance change rate. Copper particles with sizes ranging from 48 μm to 150 μm were used in the experiment, and the experimental results are in good agreement with the simulation results. By detecting the inductive change of the micro inductive sensor, the detection limit of the copper particles only reaches 70 μm. However, the detection limit can be improved to 48 μm by detecting the equivalent resistance of the inductive sensor. The equivalent resistance method was demonstrated to have a higher detection accuracy than conventional inductive detection methods for non-ferrous particle detection in hydraulic oil.

Prediction of wear trend of engines via on-line wear debris monitoring

Abstract On-line wear debris monitoring is a useful technology for real-time machine wear condition monitoring but needs further development. This study, based on previous developments of an on-line visual ferrograph (OLVF), focused on (i) data reconstruction for extracting representative and reliable wear condition related characteristics, and (ii) development of an improved model for on-line wear prediction. Wear monitoring of a diesel engine was performed using this on-line wear debris monitoring system. Experimental results and comparisons between the improved relevance vector machine (RVM) model and other models show that the improved RVM model gives an earlier warning and enhances the prediction accuracy.

Photonic Low Cost Micro-Sensor for in-Line Wear Particle Detection in Flowing Lube Oils.

The presence of microscopic particles in suspension in industrial fluids is often an early warning of latent or imminent failures in the equipment or processes where they are being used. This manuscript describes work undertaken to integrate different photonic principles with a micro- mechanical fluidic structure and an embedded processor to develop a fully autonomous wear debris sensor for in-line monitoring of industrial fluids. Lens-less microscopy, stroboscopic illumination, a CMOS imager and embedded machine vision technologies have been merged to develop a sensor solution that is able to detect and quantify the number and size of micrometric particles suspended in a continuous flow of a fluid. A laboratory test-bench has been arranged for setting up the configuration of the optical components targeting a static oil sample and then a sensor prototype has been developed for migrating the measurement principles to real conditions in terms of operating pressure and flow rate of the oil. Imaging performance is quantified using micro calibrated samples, as well as by measuring real used lubricated oils. Sampling a large fluid volume with a decent 2D spatial resolution, this photonic micro sensor offers a powerful tool at very low cost and compacted size for in-line wear debris monitoring.

Simulation of Electrostatic Oil Line Sensing and validation using experimental results

Electrostatic Oil Line Sensor (OLS) is one type of the electrostatic sensors based on electrostatic charging phenomenon and has been used in wear debris monitoring in oil lubricated tribological systems. With the advantages of high sensitivity and resolution, OLS has been proved to be an effective sensing technique for sliding, rolling and bearing contacts. However the sensing technique has so far not been widely used in industrial systems due to the difficulties in identify the exact charge sources and charging mechanisms in a complex tribological system. This paper presents the results from a simulation of the sensing mechanisms that is successfully validated by experimental results. The initial OLS simulation results have led to a better sensor design and its application.

A 3×3 wear debris sensor array for real time lubricant oil conditioning monitoring using synchronized sampling

A high throughput wear debris sensor consisting of 3×3 sensing channels is presented for real time online lubricant oil conditioning monitoring. Time division multiplexing was applied to the sensing channels for measuring responses of multiple channels using one set of measurement electronics. Crosstalk among the 3×3 sensing channels was eliminated by diodes that are connected in series with each channel. Parallel L-C-R resonance was also applied to each sensing coil to increase the sensitivity. Furthermore, a unique synchronized sampling method was used to reduce the date size 50 times. Finally, we demonstrated that the sensor is capable of real time detection of wear debris as small as 50µm in SAE 0W-5 at a flow rate of 460ml/min; the measured debris concentration is in good agreement with the estimated actual concentration. The design can be extended to a N×N sensor array for an extremely high throughput without sacrificing the sensitivity, and can potentially be used for real time wear debris monitoring for health condition of rotating or reciprocating machineries.

Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective

ABSTRACTDespite the wind industry's dramatic development during the past decade, it is still challenged by premature turbine subsystem/component failures, especially for turbines rated above 1 MW. Because a crane is needed for each replacement, gearboxes have been a focal point for improvement in reliability and availability. Condition monitoring (CM) is a technique that can help improve these factors, leading to reduced turbine operation and maintenance costs and, subsequently, lower cost of energy for wind power. Although technical benefits of CM for the wind industry are normally recognized, there is a lack of published information on the advantages and limitations of each CM technique confirmed by objective data from full-scale tests. This article presents first-hand oil and wear debris analysis results obtained through tests that were based on full-scale wind turbine gearboxes rated at 750 kW. The tests were conducted at the 2.5-MW dynamometer test facility at the National Wind Technology Center at the National Renewable Energy Laboratory. The gearboxes were tested in three conditions: run-in, healthy, and damaged. The investigated CM techniques include real-time oil condition and wear debris monitoring, both inline and online sensors, and offline oil sample and wear debris analysis, both onsite and offsite laboratories. The reported results and observations help increase wind industry awareness of the benefits and limitations of oil and debris analysis technologies and highlight the challenges in these technologies and other tribological fields for the Society of Tribologists and Lubrication Engineers and other organizations to help address, leading to extended gearbox service life.

Real-time monitoring of wear debris in lubrication oil using a microfluidic inductive Coulter counting device

A microfluidic device based on an inductive Coulter counting principle to detect metal wear particles in lubrication oil is presented. The device detects the passage of ferrous and nonferrous particles by monitoring the inductance change of an embedded coil. The device was tested using iron and copper particles ranging in size from 50 to 125 μm. The testing results have demonstrated that the device is capable of detecting and distinguishing ferrous and nonferrous metal particles in lubrication oil; such particles can be indicative of potential machine faults in rotating and reciprocating machinery.

Real-time monitoring of wear debris using electrostatic sensing techniques

In this article, electrostatic charge sensing technology has been used to monitor adhesive wear in oil-lubricated contacts. Previous work in this area using FZG gear wear rig and pin-on-disc tribometers demonstrated that ‘precursor’ charge events may be detected prior to the onset of scuffing. Possible charging mechanisms associated with the precursor events were identified as tribocharging, surface charge variation, exo-emissions, and debris generation. This article details tests carried out to investigate the contribution of wear debris. Tests were carried out on a modified pin-on-disc rig using a sliding point contact and fitted with electrostatic sensors, one of which monitored the disc wear track and the other the disc surface just outside the wear track. Baseline tests used mild wear conditions with no seeded particles added to the entrained lubricant, whereas the high wear tests entrained seeded steel particles into the contact to promote wear. The wear debris produced dynamic charge features and the overall charging activities are directly related to the wear rate (i.e. charging levels increase with increasing wear rate). There appears to be a link between the net volume loss and the charge levels, relating charge directly to the increasing rate of debris production. Wear debris due to natural wear produced positive dynamic charge features, whereas debris from the seeded tests produced negative dynamic charge features. The polarity of the charge on debris is thought to depend on which charging and wear mechanism is predominant.