Detection of Gear Fault Using Vibration Analysis (original) (raw)
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DETECTION OF GEAR RATIO AND CURRENT CONSUMPTION USING MOTOR CURRENT SIGNATURE ANALYSIS
To achieve reliable and cost effective diagnosis, Motor current signature analysis is used to investigate the use of an induction motor as a transducer to indicate the faults in multistage gearbox via analyzing supply parameters such as phase current and instantaneous power. In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. This analysis system can be used for measuring the characteristics for a perfectly working gearbox and use the data as a standard for measuring faults and defects in other gearboxes. The objective of this paper is to design and fabricate a gearbox motor current analysis system at different gear operations on no load. No load conditions on the gearbox are tested for current signatures during different gear operations. Also found the minimum power required to run on different gears and gear ratio. The motor current analysis system can be used further to specify mainly faults in the gear, misalignment of meshed gears, loss of contact of the gears and bearing wear
Mechanical fault detection in gearboxes through the analysis of the motor feeding current signature
The knowledge of the state of health of machinery gears helps developing cost effective maintenance plans, preventing costly down times caused by catastrophic failures. The widest spread strategy in industry to avoid faults and failures is based on preventive maintenance. Only its combination with a condition-based maintenance can detect early signs of potential machinery failures. Often, accurate information about the state of health of a piece of equipment is difficult to obtain. Strategies based on intelligent predictive maintenance could improve this situation. The most established method to gather information in mechanical systems using gearboxes relays in the use of accelerometers, which are expensive and whose installation is usually troublesome. The analysis of the electric signature of the electric motor that drives the gearbox provides a non-intrusive method, based on readily available information. Changes in the speed and load conditions of the gearbox produce correlated variations in the feeding current and voltage of the motor. A detailed analysis of these electrical signals can produce useful information about the state of health of the system. In this paper, a gear prognosis simulator (GPS) test bench equipped with a multistage gearbox is used to analyze different types of mechanical faults in the gears. Three fault families have been identified, high damage, moderate damage and low damage. Specific working conditions of the test bench have been selected to mimic the operation of different mechanical systems, such as machine tools or electro-mechanical actuators. The motor electrical current signature in the different conditions is analyzed to determine the health state of the gearbox. Signal descriptors (such as rms, kurtosis, peak-to-peak value, impulse factor, shape factor, etc.) are obtained from stationary speed. A selection of the most relevant descriptors has been carried, doing a one-way analysis. The results obtained reveal appreciable differences between the different faulty and nominal states of the gears, making possible the detection of the health state of the system using different advance data analysis techniques.
Shock and Vibration, 2016
Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral ...
Health Monitoring and Fault Diagnosis of Gearbox
— Gear box plays vital role in automobile industry. Therefore, there is a strong demand for their reliable and safe operation. If any fault and failures occur in Gear box it can lead to excessive downtimes and generate great losses in terms of revenue and maintenance. Therefore, early fault detection needed for the protection of the Gear box. In the current scenario, the health monitoring of the Gear box are increasing due to its potential to reduce operating costs, enhance the reliability of operation and improve service to the customers. The on-line health monitoring involves taking measurements on a machine while it is in operating conditions in order to detect faults with the aim of reducing both unexpected failure and maintenance costs. In the present paper, a comprehensive survey of Gear box faults, Motor current signature analysis (MCSA) to monitor the gearbox away from its actual location has been discussed.
Fault Diagnosis in Gears using Vibration Analysis
International Journal of Advanced Research in Science, Communication and Technology, 2024
In gearboxes, vibration stemming from load fluctuations and gear defects poses significant challenges. However, accessing and mounting vibration transducers in gearboxes can often be difficult. To address this, an experimental approach utilizing FFT analysis is employed to detect various types of gear tooth faults. By analysing vibration patterns, fluctuations in gear load gear faults can be identified effectively. This involves comparing signals from healthy and defective conditions using FFT analysis to trace sidebands of high-frequency vibrations. Validation is achieved by inputting data from an Accelerometer into LabVIEW, This comprehensive approach serves as a valuable tool for monitoring gear health under various operating conditions.
VIBRATION ANALYSIS TECHNIQUES FOR GEARBOX DIAGNOSTIC: A REVIEW
Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. In this paper, a review is made of some current vibration analysis techniques used for condition monitoring in gear fault.
VIBRATION ANALYSIS TECHNIQUES FOR GEARBOX DIAGNOSTIC: A REVIEW 1
Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. In this paper, a review is made of some current vibration analysis techniques used for condition monitoring in gear fault.
GEAR FAULT DETECTION IN PLANETARY GEARBOX USING STATOR CURRENT MEASUREMENT OF AC MOTORS
ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference, 2012
Vibration based diagnosis to detect gear tooth damage in gearboxes has been studied widely and it can assist in scheduling maintenance and reducing capital losses that may result from gearbox failures. However, such vibration based techniques are difficult to implement in planetary gearboxes due to the complex nature of measured vibration spectrum resulting from rotating planets with respect to the stationary transducer mounted on the gearbox housing. Motor current signal analysis (MCSA) provides an alternative and nonintrusive way to detect mechanical faults through electrical signatures. So far, no investigation has been reported in literature to monitor a planetary gearbox in an electromechanical drive-train using MCSA because of the difficulties in modeling the planetary gear-set such as a large number of degrees of freedom and nonlinearity associated with tooth separations. In this paper, a lumped parameter model of an electro-mechanical drive-train has been developed, which consists of a permanent magnet synchronous machine (PMSM) connected to a load through a planetary gearbox. Afterwards, a seeded tooth defect is introduced into the electro-mechanical model to show that MCSA can successfully provide valuable diagnostic information regarding the planetary gearbox failure. Finally, the time waveform, as well as, the Fourier transform and Morlet wavelet transform of the PMSM stator current are presented to demonstrate the capability to detect the gear tooth fault and its severity in planetary gearbox using MCSA.
An investigation on gearbox fault detection using vibration analysis techniques: A review
Gears are critical element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause signifi cant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration analysis has been used as a predictive maintenance procedure and as a support for machinery maintenance decisions. As a general rule, machines do not breakdown or fail without some form of warning, which is indicated by an increased vibration level. By measuring and analysing the machine's vibration, it is possible to determine both the nature and severity of the defect, and hence predict the machine's failure. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analysing the vibration signal using different signal processing techniques. This paper presents a review of a variety of diagnosis techniques that have had demonstrated success when applied to rotating machinery, and highlights fault detection and identifi cation techniques based mainly on vibration analysis approaches. The paper concludes with a brief description of a new approach to diagnosis using neural networks, fuzzy sets, expert system and fault diagnosis based on hybrid artifi cial intelligence techniques.