Papers by Keyword: Condition Monitoring

Abstract: Tool condition monitoring plays a huge role in the CNC machines in which it is used to avoid the breakage of the tools.In this paper area changed capacitive micro accelerometer is designed to measure the vibration exposure of the tool on various applications in the CNC machines. The design process and simulation of the Micro accelerometer are done using INTELLISUITE 8.6. The rectangular folded beamis designed to reduce the residual stress and to obtain a better sensitivity. Static and dynamic analysis shows that sensitivity of the designed capacitive accelerometer is good enough to detect the vibration of the tool.

Abstract: The condition monitoring of seals is very important in the working process of the hydraulic system. However, it is often difficult because of the limitation of the structure. The purpose of this study is to find an effective way to solve it. With finite element analysis (FEA) software Ansys Workbench, and the O-ring is taken as the example for the comparative study of the contact strain between the O-ring and the sealing groove under normal condition and failure mode respectively. The results show that the contact strain on the sealing groove under normal condition and failure mode are different. A certain rule is presented as the working medium pressure, pre squeeze rate, sealing groove notch radius and temperature changes. It provide an important basis for further monitoring of O-ring failure through contact strain changes in sealing groove.

Abstract: This paper presents condition monitoring of industrial gas turbine by monitoring its critical operating parameters using statistical process control. This will consequently enables the detection of any degradation of gas turbine operating parameters and thus to better prepare for any forward actions that required. Basically performance of gas turbine and its critical operating parameters degrades over time. These parameters however degrades and eventually reach the OEM recomended limits without even triggereing any earlier alerts. Therefore, corrective maintenance actions are required to bring the parameters back to an acceptable operating condition which causing downtime in operation and accounts for large maintenance together with operating costs. Hence by identifying any degradation and deviation in gas turbine parameters in advance before it reaches its OEM limit will help to improve maintenance scheduling and practices and thus enhanced the reliability of the machine. It also able to identify false alarms and shutdowns which can cause unnecessary maintenance and non profitable stops. SFC method is also found to be able to estimate the progression of component/ performance degradation and thereby generating a continuously updated prediction of the remaining useful life of machine components. SPC based machine condition monitoring uses statistical process control charts such as individual and moving range methods to create the operating threshold of the machine. These thresholds were showed to be capable to determine and identify performance degradation in advance or earlier before it reaches the OEM limits for each individual parameters.

Abstract: Over the years, condition monitoring of gear transmission systems have captured significant worldwide attention from both industries and academia. This is in light of the fact that an effective condition monitoring techniques will unquestionably extend the life span of the rotating equipment. In this research, both the vibration and temperature monitoring techniques were utilized to characterize the vibration behavior of worm gear as function of gear lubricant’s viscosity. Three different types of lubricant’s viscosity; VG100, VG460 and VG680 were used in the study to serve the sliding friction of worm gears. The predetermined speeds of electric motor at 900, 1150 and 1400 rpm were introduced to the gearbox prior to the measurement of vibration signal and temperature profile. The results revealed that a lubricant with higher viscosity contributes to less vibration amplitude. At 1150 rpm, it was recorded that the vibration amplitudes are higher compare to the other motor speeds, for all lubricant's types. In this case, VG100 showed the highest vibration amplitude followed by VG460 and VG680. This result was corroborated well with the obtained temperature profiles which are 35.0°C, 35.7°C and 39.3°C for the respective VG100, VG460 and VG680. Thus, concludes the correlation between the lubrication’s viscosity, vibration level, temperature profile and worm gear speed.

Abstract: Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

Abstract: A description of a system, developed for the condition monitoring of wind turbines, which combines innovative, real time, and on-line oil sensor technologies is described. The system integrates the measurement of the three main parameters that assess the status of the lubricating oil in the lubrication system using different technologies: the degree of oil degradation using visible absorbance spectroscopy; the water content using near infrared spectroscopy; and the presence of wear debris using image processing technology. The measuring principles, sensor integration and validation test results obtained in artificially degraded oil samples are presented.

Abstract: The shortcomings of current on-line condition monitoring system include: the research was concentrated in AC substation; the function was single and completely independent; the protocols were not compatible and the interface was not unified. To improve these disadvantages and better satisfy the needs of the condition monitoring in converter station, the design and implementation of condition monitoring and decision support system in HVDC converter station was realized. By studying of condition monitoring method, decision support technology and engineering application of HVDC project, the system design principle was discussed, the software structure was shown and the function realization was given in detail. The implementation realized real time monitoring for running equipments, provided reliable gist for maintenance and reduced the costs of resources. The implementation is applied to HVDC project witch has a great significance in improving the operation efficiency and promoting the smart grid construction.

Abstract: The aero-engine is the core part of the aircraft, which plays a very critical role in aviation safety and economy. The engine fleet management is a workable management tool, can be used in airline route operation, with the goal of improving the engine time between shop visits and optimizing maintenance costs, while maintaining the reliability of the engine in service.

Abstract: The non-contact mechanical seal end faces opening friction condition detection and the measurement of film thickness when the end faces is just-lift-off, which are always key problems for scholars engaged in sealing industry for many years, but there is no effective solution. Acoustic emission (AE) signal generated in the running process of mechanical seal end faces has a plenty of information about the faces contact state. According to this, the thickness measurement of mechanical seal and opening condition monitoring technology by using particle filter are put forward based on the acoustic emission signal. Acoustic emission sensor is installed in the stationary ring seat, used for the indirect outer detection of the dynamic and stationary rings opening condition. The acoustic emission signals are processed by particle filter technology, and then the signal features are extracted in time domain, frequency domain and wavelet packet energy. A BP neural network model is established, the features of signal characteristic used as input of the model. It is finally realized that the mechanical seal end faces opening condition was recognized and the film thickness was measured. Eddy current sensor is installed inside the stationary ring and used for direct measurement of film thickness and verify the results got from the BP neural network. Through the experiment, this method is practical and effective, and which can be used in the monitoring of mechanical seal end faces working condition in industrial field.

Abstract: Intelligent sensing system for distribution line condition monitoring is presented. It uses sensing terminals to detect load current, temperature, humidity and water pass state. It uses intelligent sensor to collect the distribution condition information. The information collected by the terminals transmits to intelligent sensing system. It is designed without the need for primary equipment reform, without the need of large-scale construction of communication channel, without the need for power blackout with small investment and big benefit. It greatly improves the power supply capacity, efficiency of the system accident treatment and the reliability of power supply.