On-line Visual Ferrograph Research Articles

Multisensor information integration for online wear condition monitoring of diesel engines

A diesel engine bench test was performed, and the online visual ferrograph (OLVF) and performance monitoring sensors were used to evaluate engine wear. The sliding window method was used to segment OLVF-monitoring data; features such as probability of smaller value and accumulated wear coefficient were extracted to clarify wear degree. The weighted combination multisensor information integration method was developed to calculate current engine condition factors. The results show that OLVF monitoring exhibits more sensitivity than other performance monitoring sensors. Using multisensor information provides an early warning of performance degradation ~40h before the diesel engine experiences a catastrophic fault.

Characterization of wear-debris group in on-line visual ferrographic images

Wear-debris characterization using ferrography, which is of importance in machine condition monitoring and fault diagnosis, remains a challenge. The newly developed on-line visual ferrograph can provide digital wear-debris images and perform on-line analysis during wear monitoring. In this article, image projection transformation was utilized for extracting the overall characteristics of the wear-debris chains according to the fundamental feature of wear-debris arraying along the horizontal direction. Moreover, Full Binary Tree Based Image Division was also proposed to analyse the regional features on different scales in on-line visual ferrographic images. Several descriptive parameters including thinning ratio, chain length and chain width were proposed. In the experiments, four types of images with different wear-debris groups were compared and a group of time-sequence on-line visual ferrographic images of an inline four-cylinder gasoline engine was studied. It is found that more comprehensive wear information can be acquired through the proposed multi-parameter description method. Meanwhile, the image projection transformation method extracts macrocharacteristics of images rapidly and efficiently and the thinning ratio measures wear-debris width in an intuitive way.

Wear Condition Monitoring and Working Pattern Recognition of Piston Rings and Cylinder Liners Using On-Line Visual Ferrograph

This study applies stepped loading and stepped velocity approaches to simulate the actual working conditions of gasoline engines. Accelerated wear tests were conducted for piston rings and cylinder liners under different lubricating conditions by using a self-made pin-on-disc wear machine equipped with an on-line visual ferrograph (OLVF) for wear monitoring. The wear coefficients for oil monitoring were extracted to distinguish between constant conditions and stepped changing conditions. A similarity model for oil monitoring was constructed and the monitoring data sets of similar working conditions were grouped together. Results show that the OLVF monitoring system can be used to obtain the real-time variation in debris concentration. The index of particle coverage area (IPCA) of OLVF increases abruptly after the load or speed changes. The similarity model can evaluate the similarity of the variation trend of IPCA under different operating conditions. The relationship between IPCA and working conditions was examined in this study and provides an essential support to wear monitoring and life prediction of engines.

Assessment of Antiwear Properties of Lube Oils Using Online Visual Ferrograph Method

The four-ball wear test is currently a mainstream method for assessing antiwear properties of lube oil. However, the assessment is normally based on a comparison of the final wear scar diameter and wear progressions cannot be well presented. A new experimental method for assessing antiwear properties of lube oil with near real-time (NRT) wear information was developed. Wear debris from a ball-on-disc tribometer under boundary lubrication was deposited by an online visual ferrograph (OLVF) every 6 min, and NRT information on wear debris generation rate was obtained. A mathematical model of the lubrication system serving the ball–disc tribopair was built, and an index of particle coverage area (IPCA) was approximately proportional to wear rate (by weight) of the ball–disc tribopair. IPCA curves characterized the antiwear performance of lube oil by three indictors, running-in duration, index of running-in wear amount, and index of steady-state wear rate. With the three indictors, a rule assessing the antiwear performance between two oils was proposed. Accordingly, wear tests of eight oils were performed, and their antiwear properties were distinguished by the assessment rule. Under the same setting, the assessing experiment of the same oil was conducted five times. According to the statistical analyses, the experimental method exhibited considerable repeatability.

Full-life dynamic identification of wear state based on on-line wear debris image features

Wear state identification is a bottleneck for the monitoring of engine's condition due to its complex characteristics as system-dependent, time-dependent and physical coupling. Correspondingly, full-life dynamic identification of the wear state of an engine in service was investigated for real-time performance evaluation. As wear information carrier, images of wear debris carried by the cycling lubricant were sampled by an OLVF (On-line Visual Ferrograph) sensor. Two characteristic indexes including IPCA (Index of Particle Coverage Area) and EDLWD (Equivalent Diameter of Large Wear Debris) extracted from the on-line wear images, were adopted to characterize the wear state quantitatively by representing wear rate and mechanisms, respectively. A dynamic feature-matching model for real-time identification was studied comprehensively by referring to the stage features of wear state variation. Furthermore, a one-class model was built using the SVDD (Support Vector Data Description) method for categorizing statistical samples. By integrating the feature-matching and de-noising methods, a good identification was achieved with those samples. On this basis, a stage-based model for real-time wear state monitoring was built and verified with time-sequence monitoring samples from an engine bench test. The method shows potential as a promising on-line wear state evaluation tool, especially for full-life monitoring.

A wavelet-analysis-based differential method for engine wear monitoring via on-line visual ferrograph

Wear over time affects engine’s reliability and efficiency. On-line wear monitoring could provide timely information about engine health condition. In the current study, on-line monitoring of engine wear via an on-line visual ferrograph was performed in reliability tests of gasoline engines, and a wavelet-analysis-based differential method of data analysis for wear condition estimation was proposed. The tests were designed for 220 h, which consist of a running-in stage of 20 h and a thermal shock cycle test stage of 200 h. One of the tests was terminated because of failures in the main bearings and crankshaft journal at 146th hour of thermal shock cycle test, while the other two completed successfully without failures. Index of particle coverage area, which represents wear-debris concentration in lube oil, was studied, and piecewise trend-extraction of index of particle coverage area was achieved by wavelet decomposition and reconstruction. The first-order differential of the index of particle coverage area trend was used to represent the wear rate or the generation rate of debris for health condition assessment of engines. Off-line oil analyses were performed in laboratory via an analytical ferrograph, and engine disassembly results of the engines were given to determine the causes of engine failures if it happened. It is found that favorable trend extraction from index of particle coverage area could be achieved by the segmented wavelet de-noising. Moreover, on-line visual ferrograph monitoring estimated the engine wear at macro-levels effectively, and it provided an advance warning for the failures after the continued deterioration of the engine wear. The study and application of this method can make early failure prediction of engine and avoid serious fault.

A Quantitative Model of Wear Loss Based on On-Line Visual Ferrograph

On-line visual ferrograph (OLVF) is an efficient and real-time condition monitoring device. From the point of flow conservation, on the basis of the particle coverage area data collected by OLVF, this paper deduced two models about wear loss of the tribo-pairs in the wear process, one is general mathematical (GM) model including distribution impact factor of wear particle, and other simplified GM (SGM) model which does not contain the factor. The key factor affecting the accuracy of the two models is the three dimensional information of wear particles referring to particle area and thickness. This model using the disc and the ball whose materials were GCr15 were experimentally demonstrated on a pin-on-disc testing machine. And the OLVF was used to acquire the coverage area of the wear particles, which can reflect the wear loss. It shows that, in some cases, the approximate wear loss in the process was obtained on-line conveniently. Compared with experiment values derived from other wear measurement methods like weighing mass method and surface profilometry method, the SGM model can reflect tendency of wear loss about the tribo-pairs continuously. The deviations about wear loss by the model were discussed. Meanwhile, compared with the traditional means to compute the wear loss, this SGM model could be employed both for off-line analysis and on-line condition monitoring programs.

Description of Wear Debris from On-Line Ferrograph Images by Their Statistical Color

Analytical ferrography has been proved to be one of the most popular methods for wear characterization. However, it is limited by the real-time requirement of condition-based monitoring. A new wear characterization by on-line ferrograph images is proposed. The color of wear debris was studied based on an on-line visual ferrograph (OLVF) sensor. Generally, the features of on-line ferrograph images included low resolution, high contamination, and wear debris chains. The weak color of the wear debris, especially nonferrous metal debris, in an on-line ferrograph image was unavoidably merged into the mass noises. Accordingly, the on-line images were converted from the initial red, green, blue (RGB) format into hue, saturation, intensity (HSI) for the description of color images. The transmitted image was binarized to locate all wear debris and the wear debris was extracted by their pixels from the corresponding reflected image. The distributions of two HSI components, hue and intensity, were used to characterize the color of on-line ferrograph images. Aiming at the global noise induced by uneven light during sampling, the distributions of the hue and intensity of the wear debris were subtracted by that of the reflected image. As a result, the statistical colors of wear debris were extracted with the hue and intensity from the on-line ferrograph images. A designed experiment with manually prepared oil samples revealed that the wear debris of three common metals could be well differentiated according to their colors via the on-line ferrograph images. The method provides a primary exploration on describing the color of wear debris by on-line ferrograph images.

Engine Wear Monitoring with OLVF

In this article, an on-line visual ferrograph (OLVF) was used to monitor the wear condition of a vehicle engine during a bench test. A quantitative index of particle coverage area (IPCA) and wear debris ferro-image were obtained via OLVF to characterize wear degrees. On-line ferrograph analysis indicated some problems with the engine from about the 17th hour. A linear threshold value method to identify running condition of engines with the OLVF data was proposed. The OLVF data were in a fault zone after running for 25 h, when a base bolt broke and the engine stopped working during the test. The engine was disassembled and inspected after the test. Several wear scars were found on the cylinder walls as well as on the bearing surfaces. The oil was added at the 26th and 55th hours, which was also reflected by the variation of IPCA. Moreover, several off-line analyses were carried out in parallel. Off-line ferrograph data did not give any information about the abnormality and only showed stable wear debris content. Spectrometric oil analysis showed that there was no significant change in contents of metal elements.

Wear Characterization by an On-Line Ferrograph Image

Wear characterization with ferrography is an effective off-line method for monitoring a machine's wear condition. The new challenge of real-time wear reporting appeared in condition-based maintenances. Newly developed on-line technologies focus on wear debris concentration rather than on ferrograph images. However, wear rate and wear mechanism are two necessary aspects in describing the wear condition. An on-line ferrograph image provided by an on-line visual ferrograph sensor gives a new solution. By analysing the features of an on-line ferrograph image, new wear characterization with rapid and statistical analysis was investigated for on-line wear reporting. The focus of this method is wear debris chains rather than single wear debris as focused on in traditional ferrography. First, an on-line ferrograph image is preprocessed into a binary image with improved quality. Then the binary image is transformed into a projection curve by parallel radon transformation. The low-frequency component characterizing wear debris chains is extracted from the projection curve by wavelet transformation, which shifts the focus on wear debris chains other than single wear debris. Spectrum analysis is performed to extract the wear characteristics from the low-frequency component of the projection curve. As the main result, two statistical indexes are constructed, as value of point to point and equivalent diameter of larger wear debris, representing the statistical wear debris concentration and the equivalent diameter of larger wear debris, respectively. The indexes correspond qualitatively to wear rate and wear mechanism, respectively. The method was further examined with on-line images from the bench tests of the Eaton engine and the gear reducer of the mine scraper conveyor. The results show that the indexes are effective and independent in describing wear condition, which are promising prospects for on-line wear monitoring with ferrography.

Improvement on On-line Ferrograph Image Identification

A newly developed on-line visual ferrograph(OLVF) gives a new way for engine wear state monitoring. However, the reliability of on-line wear debris image processing is challenged in both monitoring ship engines and the Caterpillar bench test, which weren’t reported in previous studies. Two problems were encountered in monitoring engines and processing images. First, small wear debris becomes hard to be identified from the image background after monitoring for a period of time. Second, the identification accuracy for wear debris is greatly reduced by background noise because of oil getting dark after running a period of time. Therefore, the methods adopted in image processing are examined. Two main reasons for the problems in wear debris identification are generalized as follows. Generally, the binary threshold was determined by global image pixels, and was easily affected by the non-objective zone in the image. The boundary of the objective zone in the binary image was misrecognized because of oil color becoming lighter during monitoring. Accordingly, improvements were made as follows. The objective zone in a global binary image was identified by scanning a column of pixels, and then a secondary binary process confined in the objective zone was carried out to identify small wear debris. Linear filtering with a specific template was used to depress noise in a binary image, and then a low-pass filtering was performed to eliminate the residual noise. Furthermore,the morphology parameters of single wear debris were extracted by separating each wear debris by a gray stack, and two indexes, WRWR (relative wear rate) and WRWS (relative wear severity), were proposed for wear description. New indexes were provided for on-line monitoring of engines.

A New On-Line Visual Ferrograph

An on-line visual ferrograph (OLVF) characterized by direct reading and on-line analysis was developed based on magnetic deposition and image analysis. A digital sensor was integrated with a CMOS image sensor to obtain images of deposited wear debris under illumination conditions. An electromagnetic instrument was designed to deposit the wear debris flowing through an oil flow channel. The oil flow channel, fixed on the electromagnet, was arranged parallel to the magnetic flux in the air gap between two electromagnet poles. The deposition effect on wear debris was analyzed theoretically. The result shows that the wear debris in different sizes can be deposited in the same zone by controlling the oil flow rate and magnet field intensity. Corresponding application software for image sampling and processing was developed. An index of relative wear debris concentration, IPCA (Index of Particle Coverage Area), is given as an output in addition to wear debris images. Finally, two kinds of experiments were specified to assess the effect and validity of the OLVF. The results show that the OLVF has effective deposition and identification for both relatively large and small wear debris with rational control parameters. The validity examinations with the commercial particle quantifier (PQ) and direct reading ferrograph (DR) show that the OLVF has an approaching trend to the reference instruments in both heavily and lightly contaminated oil.

Journal Bearing Wear Monitoring via On-Line Visual Ferrography

Wear condition of journal bearing was experimentally monitored with an On-Line Visual Ferrograph (OLVF) system. The round bearing was made of 45# steel with babbitt alloy bushing and the bearing journal was made of 45# steel. High stress of the water content of 0%,1%,3% in lubricant, 10 times of a normal load of 2200 N, and the rotating speeds at 500, 1000, 2000 rpm were adopted in accelerated experiment. A quantitative index of particle coverage area (IPCA) together with wear debris ferro-image was used to characterize wear degree and wear mechanisms. Three wear stages with corresponding wear debris ferro-images were identified from IPCA variations characterized by bathtub curve trend. The first one was running-in stage characterized by the highest wear debris producing rate and higher large debris content. The second one was normal wear stage characterized by the lowest wear debris producing rate and small normal debris. The final one was failure initiation stage characterized by higher wear debris producing rate and higher large debris content. The corresponding dominant wear mechanisms were micro-ploughing and micro–cutting induced by rough initial surfaces in the running-in stage, local rub due to vibration in the normal stage, and fatigue and abrasive wear in the failure initiation stage.