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Ball bearings in gas turbine have played a critical role in supporting heavy radial loads but with higher failure rates and repair costs. Therefore, the purpose of this study is to introduce and study a method for their failure analysis with an actual industrial example to guarantee operation reliability and safety.

Spectrometric oil analysis was used as an early abnormal wear indicator, based on which emergent in-use oil replacement was carried out to reduce the wear rate. However, with wear deterioration, further wear failure investigation was conducted by LaserNet Fines and ferrography to detect the imminent wear failure. Finally, with the assistance of elemental analysis of the typical wear particles, the root cause and worn components were determined by scanning electronic microscope and energy-dispersive X-ray spectroscopy.

The results have shown that an extraneous source led to wear failure, which later caused overheat between the outer bearing ring and ball. It is in accordance with visual inspection of the disassembled engine.

This method has specified the occasion under which the suitable measurement can be taken. It can achieve the rapid wear condition assessment allowing for root cause and worn parts identification. In addition, wear rate reduction by change of oil can be efficient for most of the time to avoid premature disassemble, especially with the possibility of contamination. It has provided experience to address similar industry-level practical wear failure analysis problems.

Most of the mechanical systems consist of common tribo components such as gear, bearing, seal, pump, etc. During operation, the failure of such a component may lead to failure of the system or any other components depending on the system structure or its connections. The transformation of functional quantities or parameters within and various components make these vulnerable to failures and breakdowns. The main purpose of this paper is to evaluate the reliability of a tribo‐pair during operation based on the operational parameters which takes into account the deviation in functional objective and considers the component structure explicitly.

Failure representation using digraph models have mainly been limited to chemical systems and processes but have been applied to a limited extent to mechanical systems. Researchers have considered the operating parameters such as pressure, temperature; feed rate, flow, etc. as the input/output operational parameter for failure cause identification of tribo mechanical system. However, these are not true in case of components like gear, bearing, etc. Therefore, this methodology has been refined in this paper, and is extended to a tribo‐pair by considering its functions along with operating parameters for reliability assessment. The tribo‐pair considered for the development of model is a gear pair, which is a common and important example of mechanical components.

Understanding of the failure modes helps the designer in identifying the root cause of failure and the operational parameters whose increase/decrease affects the functional objective. Consideration of the input and output parameters and their interrelations are used to develop input‐output model of a gear pair called gear pair model. This requires consideration of the input and output parameters, which are based on the gear pair functionality. These parameters have been identified. Four input parameters; five output parameters and one condition monitoring parameter are considered to model the gear pair. In addition to input and output parameters condition monitoring parameters are also considered to develop the gear pair model.

Reliability assessment of a tribo‐pair during operation will help the practicing engineers to take corrective action and minimize the occurrence of undesired failure symptom during operation. The decrease in possibility of undesired failure symptom will enhance the reliability of mechanical system.

The paper aims to study the wear and breakage characteristics of coated carbide cutting tools through micro-milling slot experiments on superalloy Inconel 718.

During the micro-milling process, the wear and breakage appearance on the rake face and flank face of the cutting tools, as well as the failure mechanism, have been studied. Furthermore, the wear and breakage characteristics of the micro-cutting tools have been compared with the traditional milling on Inconel 718.

The main failure forms of the micro tool when micro-milling Inconel 718 were tool tip breakage and coating shed on the rake and flank faces of the cutting tool and micro-crack blade. The main causes of tool wear were synthetic action of adhesive abrasion, diffusion wear and oxidation wear, while the causes of abrasive wear were not obvious.

The changing trend in tool wear during the micro-milling process and the main reasons of the tool wear are studied. The findings will facilitate slowing down the tool wear and prolonging the tool life during micro-milling Inconel718.

The results of this paper can help slow down the tool wear and realize high efficiency, high precision and economical processing of small workpiece or structure of the nickel-based superalloy.

This study provides a unique integrated diagnosis system to investigate the causes of low productivity, profitability, machinery health conditions and wear severity of medium-size biscuit industry assets in Taiz, Yemen.

The evaluation is based on an integrating of the overall equipment effectiveness (OEE) and oil-based maintenance (OBM) approaches. The data are collected using the company's operational records, interviews and observations, while the used lubricating oil samples are also collected from production lines' machineries. Scanning electron microscope (SEM) is used to study the wear debris particle features and wear mechanism. Different other analysis tools such as fishbone, 5 whys and Pareto charts are also used to investigate the root causes and plausible recovery solutions of machinery failures.

This study demonstrated that a large proportion of machinery failures and production loss are of management concerns. Also, this study inferred that the analysis of wear debris is unique and informative for determining machinery wear severity and useful life. Finally, the current conditions of production lines are clarified and suggestions to use a mixed preventive/predictive maintenance management approach are also elucidated.

This work implemented an integrated OEE/OBM diagnostic maintenance system to investigate the root causes of low productivity and machine failures in real production lines and suggested robust decisions on the maintenance duties.

Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.

Using the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.

The analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.

The major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.

To demonstrate the theory and effectiveness of reliability‐improvement countermeasures for line equipment, specifically industrial robots for automotive production engineering.

Suggests an efficient method of life‐cycle maintenance. The defects of industrial robots are analysed using Weibull analysis.

From the analysis, a strategy of countermeasures is framed for component screening, reliability design and lifetime estimation.

This method has been implemented worldwide in Toyota's factories, and has produced a better operating life cycle for industrial robots.

Summarizes briefly the dramatic advances made in the reliability of mechanical seals for rotating shafts in the process chemical and petrochemical industries over the last 30 years. Shows that expected mean time before failure has improved from tens of days to years over that time.

An optimal maintenance program for electrical power system components should be based on their reliability. Since, for components characterized by high reliability and cost such as HV circuit breakers, available statistical data are in limited number, a physical model for their ageing is opportune. In the paper a Predictive Maintenance Program (PMP), for determining when a HV circuit‐breaker should be rebuilt, is formalized; it is based upon an adequate stochastic model of electrical wear associated with breaking operations due to system faults. In the model, both fault times and amplitudes are described by means of random variables, in order to deduce a reliability function used as input data for a Bayesian discriminant analysis which dynamically estimates, also in the presence of observation errors, the state of the component, determining the optimal times to perform a maintenance action.

The aim of this study is to determine the variation of the different oil analysis instruments in terms of standard deviation and CV‐values, when measuring samples of fully formulated hydraulic and gear oils taken from working systems.

In this investigation, two different spectrometric techniques, inductively coupled plasma‐optical emission spectrometers (ICP‐OES) and rotating disk electrode‐optical emission spectrometers (RDE‐OES), have been studied to determine the instruments' precision of measurement and ability to measure the absolute level of contamination. The study was based on a series of measurements using artificial contamination mixed with oil.

The ICP has better precision of measurement of the two instruments, but cannot predict the absolute values of contamination when oil samples are only treated by organic solvent dilution if the samples include large or dense particles. It is therefore not too good, with the sample pre‐treatment method used, at detecting wear processes that produce dense/large particles, such as pitting failure. For instance, microwave‐assisted acid digestion could be used for sample pre‐treating to obtain accurate results in that case. It should, however, be able to detect wear mechanisms that produce small particles such as abrasive wear in any case. The ICP has a repeatability value of r=3 percent and a reproducibility value of R=12 percent for contamination levels of between 50 and 400 ppm and r=0.6  and R=2 ppm, respectively, at values below 50 ppm. The RDE cannot predict the absolute value of contamination if this includes large or dense particles if proper sample pre‐treatment is not used. It is therefore not good at detecting wear mechanisms that produces dense/large particles (if the oil samples are not pre‐treated properly) such as pitting but should be able to detect abrasive wear and similar processes that produce small particles in any case. The RDE's precision of measurement is not as good as the ICP, with a reproducibility variation of R=r=25 percent for contamination levels between 20 and 500 ppm and R=r=6 ppm for contamination level below 20 ppm.

Only the effects from lubricating oils are studied.

This study will significantly increase the industrial knowledge concerning measurement precision in particle contamination measurement systems.

No similar study is found.

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.