Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals (original) (raw)

Modern Vibration Signal Processing Techniques For Vehicle Gearbox Fault Diagnosis

2014

This paper presents modern vibration signalprocessing<br> techniques for vehicle gearbox fault diagnosis, via the<br> wavelet analysis and the Squared Envelope (SE) technique. The<br> wavelet analysis is regarded as a powerful tool for the detection of<br> sudden changes in non-stationary signals. The Squared Envelope<br> (SE) technique has been extensively used for rolling bearing<br> diagnostics. In the present work a scheme of using the Squared<br> Envelope technique for early detection of gear tooth pit. The pitting<br> defect is manufactured on the tooth side of a fifth speed gear on the<br> intermediate shaft of a vehicle gearbox. The objective is to<br> supplement the current techniques of gearbox fault diagnosis based<br> on using the raw vibration and ordered signals. The test stand is<br> equipped with three dynamometers; the input dynamometer serves as<br> the internal combustion engine, the out...

Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acoustic Emission Technique

Maintenance is a set of organised activities that are carried out in order to keep an item in its best operational condition with minimum cost acquired. Predictive maintenance (PdM) is one of the maintenance program that recommends maintenance decisions based on the information collected through condition monitoring techniques, statistical process control or equipment performance for the purpose of early detection and elimination of equipment defects that could lead to unplanned downtime of machinery or unnecessary expenditures. Particularly Gears and rolling element bearings are critical elements in rotating machinery, so predictive maintenance is often applied to them. Fault signals of gearboxes or rolling-element bearings are nonstationary. This paper concludes with a brief discussion on current practices of PDM methodologies such as vibration analysis and Acoustic Emission analysis, which are widely used as they offers a complimentary tool for health monitoring or assessment of gears in rotating machineries.

A Comparative Study of Various Methods of Gear Faults Diagnosis

Journal of Failure Analysis and Prevention, 2014

Investigating gear damages using vibration signal is a subject of a high interest, because gears vibration signals are complex and difficult to understand. A failure diagnosis of gearbox based on Fourier analysis of the vibration produced by speed reducers has shown its limits in terms of spectral resolution. In the present paper, a comparative study of the performances of various different methods of fault diagnosis of helicopter gearbox gear is carried out. The results are highlighted on the basis of real data recorded during a helicopter flight and have showed that cepstral analysis is most effective technique in detecting gearbox gear faults. Keywords Time domain analysis Á Fast Fourier transforms (FFT) Á Amplitude modulation analysis Á Cepstral analysis Á Synchronous averaging technique Á Helicopter gears

IJERT-Automation of Gearbox Fault Diagnosis using Sound and Vibration Signal

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/automation-of-gearbox-fault-diagnosis-using-sound-and-vibration-signal https://www.ijert.org/research/automation-of-gearbox-fault-diagnosis-using-sound-and-vibration-signal-IJERTV3IS051908.pdf Gears have wide variety of applications. They form the most important component in a power transmission system. Advances in engineering technology in recent years have brought demands for gear teeth, which can operate at ever increasing load capacities and speeds. The gears generally fail when tooth stress exceeds the safe limit. Therefore, it is essential to determine the maximum stress that a gear tooth is subjected to, under a specified loading. Analysis of gears is carried out so that these can be prevented from failure. When failure occurs, they are expensive not only in terms of the cost of replacement or repair but also the cost associated with the downtime of the system of which they are a part. Reliability is thus a critical economic factor and for designer to produce gears with high reliability they need to be able to accurately predict the stress experienced by the loaded gear teeth. This paper deals with the automation of fault diagnosis process of gearbox using sound and vibration signals obtained from the gearbox with 3 different fault conditions, 3 different speeds and 3 different loads. Sound and Vibration time domain signals are obtained for all the above conditions and the obtained signals for faults are compared with the time domain signals of good gear.

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.

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.

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.

Gearbox faults identification using vibration signal analysis and artificial intelligence methods

2014

The paper addresses the implementation of feature based artificial neural networks and vibration analysis for the purpose of automated gearbox faults identification. Experimental work has been conducted on a specially designed test rig and the obtained results are validated on a belt conveyor gearbox from a mine strip bucket wheel excavator SRs 1300. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for selforganized feature maps and based on the results a reduced set of vibration features are used as inputs for supervised artificial neural networks. Two typical gear failures were tested: worn gears and missing teeth. The achieved results show that proposed set of vibration features enables reliable identification of developing faults in power transmission systems with toothed gears.

Vehicle gearbox fault diagnosis using noise measurements

Journal homepage: …, 2011

Noise measurement is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of noise measurement in monitoring gearboxes this has been solely applied on any types of gears (spur, helical, ..etc.). The condition monitoring of a lab-scale, single stage, gearbox, represents the vehicle real gearbox, using non-destructive inspection methodology and the processing of the acquired waveform with advanced signal processing techniques is the aim of the present work. Acoustic emission was utilized for this purpose. The experimental setup and the instrumentation are present in detail. Emphasis is given on the signal processing of the acquired noise measurement signal in order to extract conventional as well as novel parameters potential diagnostic value from the monitoring waveform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with most interesting diagnostic behavior are highlighted. The present work also reports the results concluded by long term (~ 6.0 h) experiments to a defected gear system, with a transverse cuts ranged from 0.75 mm to 3.0 mm to simulate the tooth crack. Different parameters, related by the analysis of the recording signals coming from acoustic emission are presented and their diagnostic value is discussed for the development of a condition monitoring system.