Papers by Keyword: Condition Monitoring

Abstract: The nonlinearities, induced by structural looseness or wear/fatigue of components, are good indicators of the health condition of a machine or structure. However, most existing condition monitoring techniques were initially designed for dealing with linear systems, thus unable to account for these scenarios properly. A few available nonlinear techniques are tried in condition monitoring. However, they are more or less limited owing to either intensive computation or unsatisfactory sensitivity to incipient abnormalities. In view of this, a new fractal analysis-based condition monitoring technique is researched in this paper. Firstly, a few number of fractal analysis methods with efficient computing algorithms are investigated in order to find an ideal one for condition monitoring application. Subsequently, a detailed investigation was conducted to verify the favored method and understand its instantaneous properties, robust performance against noise, and sensitivity to the abnormalities. Finally, following discussing the window width used in practical calculation, the condition monitoring technique developed based on the favored fractal analysis method is validated experimentally. Experiments show that the proposed technique does provide an efficient and successful nonlinear tool for machine operation condition and structural health condition assessment.

Abstract: Along with the rapid economic development, the large power-generating wind energy conversion systems are increasing both in number and in size. Because of the high construction cost, the consequence of any structural failure, especially offshore where replacing a damaged member such as blade which weights tones, is pretty expensive. Consequently particular actions are required to the protection of these structures. Among these many strategies, continuously monitoring of the condition of the system which provides a predication long before the actual failure should be one of the most efficient ways. In the paper, the main problems and their corresponding surveillance schemes of the system including tower, foundation, blade and turbine nacelle are discussed.

Abstract: Different techniques have been developed in the area of bearing wear monitoring. This paper proposes a different experimental study on bearing wear monitoring by using an airborne technique. The data captured in the airborne technique will be analyzed by using I-kaz^TM Multi Level (⁷Z) coefficient and then will be correlated with the conventional specific wear rates, K. The wear tests were carried out by using a pin-on-disc configuration at a sliding speed of 7.85 m/s. A set of sliding distance ranging from 20 160 km at a fixed load of 200 N was utilized and the K value was measured at every interval of 20 km for the speed. SAE40 type lubricant was used in the test to simulate the actual operation of the connecting rod bearing. The audio range frequency below 20 kHz in the airborne technique was obtained through a microphone 40SC type which was placed 10 mm from the pin-disc contact. The analysis result showed that the wear rate, K increased from 1.82 to 6.70x10^-8 mm³/Nm as the sliding distance increased, indicating that a mild-abrasion wear regime had occurred. The curve fitting of K as a function of I-kaz^TM Multi Level coefficient showed a similarity to an established of Taylor Tool Life curve. Thus, it was possible to correlate the Taylor curve and worn bearing, mainly in monitoring and identifying the bearing condition with respect to the sliding distance. The trend of I-kaz^TM Multi Level coefficient was found to be consistent with the increase of sliding distance which indicates that the I-kaz^TM Multi Level value can positively be used as wear response indicator for bearing.

Abstract: Gas-turbine engines are critical to the operation of most industrial plants, aircraft and heavy vehicles. Condition monitoring is essential to mastering mechanical system running status, improving operation reliability, and reducing maintenance cost. This paper reviews state-of-the-art gas turbine condition monitoring, puts forward the pending problems and predicts future development in the field. Three main advanced methods are introduced briefly in the end.

Abstract: Based on a cutting dynamics model, a new digital machining process model that integrates workpiece quality and machining condition information is proposed, and the mapping relationship between machine tool structural dynamics and product quality information (e.g., dimension error, surface shape) is analyzed in detail. On the basis of the digital machining process model, the condition monitoring and fault diagnosis method of the machining process is investigated, and an implementation flowchart of the method is proposed with explanations of critical techniques. Finally, the proposed model is applied to the condition monitoring and fault diagnosis of the machining process of a CNC end milling machine. The damage of its spindle bearing is detected successfully, which provides the proof for the root cause identification of machining error in the digital machining process.

Abstract: Defect detection in pipes is an essential task specially for sensitive applications such as oil and gas industry where special cares are required. Corrosion is a common defect in pipes which has attracted attention of researchers. In present work a non-destructive methodology for pipe corrosion monitoring is introduced. Polymer of Vinylidene Fluoride (PVDF) Piezoelectric is used as the sensor to measure strain variations affected by internal corrosion. High sensitivity and low cost of piezoelectric materials made them a good candidate for precise industrial applications. Different corrosion conditions (i.e. corrosion location along pipe and corrosion depth) are modeled and sensors voltages in different corrosion conditions are simulated. Finally in order to develop an effective corrosion detection system, an Artificial Neural Network (ANN) is designed to recognize position and amount of corrosion according to sensors voltages. The ANN performed corrosion condition recognition with 91 % of accuracy. This method provides the capability of online implementation for continuous maintenance of pipelines.

Abstract: Rotating machinery becomes more and more large and complex, increasingly high degree of automation. Rotating machinery fault could easily lead to heavy losses. Therefore, the requirements of monitoring and diagnosis systems are increasing high. In this paper, the superiority of the application of virtual instrument on condition monitoring and diagnosis system building in the industrial production is described. And then, the rotor system as the main research object, a rotating machinery condition monitoring and diagnosis system is built by using Virtual Instrument technology. At the same time, the structure of the condition monitoring and diagnosis system is discussed. Besides, data acquisition process and fault features recognition method are discussed as well. Finally, the correctness and accuracy of fault detection are verified by means of experiments.

Abstract: Cutting tool wear degrades the machining quality and reliability of CNC machine tool significantly in machining processes. Methods for monitoring tool wear online are therefore crucial to implement optimization of the cutting parameters and improvement of manufacturing processes performance. An intelligent tool wear estimation system that integrates condition monitoring, pattern recognition and trend prediction has been presented in this paper. The raw signals contain useful information from several sensors measuring process variables are acquired and analyzed utilizing monitoring units. The obtained feature elements are processed using support vector machine algorithm to identify tool wear degree. The implementation mode and specific functions of the integrated system architecture is detailed described. The experimental results show that the integrated tool wear monitoring system is feasible and effective.

Abstract: This paper aimed at NC system’s high-speed, high-accuracy, high reliability requirements. The condition monitoring, faint information extraction and fault diagnosis technology are researched of complex CNC system. Elaborated to develop a common interface and achieved the seamless interaction of CNC system and reconfigurable embedded monitoring unit basis. A machine tool monitoring diagnostic verification platform with on-machine monitoring and intelligent maintenance features of CNC system is constructed based on the internet. Integration achieved the CNC machine tools’ status remote monitoring and fault diagnosis, and detailed analysis of the key technologies for the components of the system. Through effectively integration of the computer technology, weak signal extraction technology, fault diagnosis or other technologies to enhance the automation, networking and intelligent level of the complex CNC system.

Abstract: This paper proposes a new method of motor fault detection by applying the eliminated-signal as data sources for motor fault analysis. Bi-spectrum is used as a key method for processing the signal. The expectation is that the eliminated-signal may contain information for fault analysis. The spectrum and bi-spectrum of the signal are applied as signal processing methods to analyze the motor faults. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly. They seem also to be able to measure fault severity levels by observing the change in among harmonic amplitudes.