The Influence of the Sound Pressure Level on the Identification of the Defects Severity in Gear Transmission by the Sound Perception (original) (raw)
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Experimental study of combined gear and bearing faults by sound perception
Keywords: vibro-acoustics sound perception gear faults bearing faults combined faults One presented in this work a vibro acoustic analysis of various signals in the case of one or several combined defects such as bearings and gears defects. The objective is to identifying each of the defects even when it combined. We begin by studying the temporal and spectral scalar indicators; a perceptive analysis of the sounds corresponding to different types of defects have been established to investigate the sensitivity of listeners to the combined defects, and the ability to distinguish between defects with different types and natures. According to the study of the vibrational indicators and of the listening test, the results are well preventative of the evolution of different defects gravity. For sound perception, the listeners could classify the sounds according to the type and the level of defects gravities.
Measurement, 2020
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Analysis of Vibrations and Noise to Determine the Condition of Gear Units
Advances in Vibration Analysis Research, 2011
Sound waving travels along paths ri of various lengths from the elementary acoustic source V(x j) to an individual microphone on the ring of an acoustic camera (Fig. 1). Paths travelled by sound waving |r i | are of different lengths and, consequently, signal delays Δi of the same sound waving, produced at the elementary sound source V(x j), are different as well.
Correlations Between Noise Level and Mechanical Vibrations Emitted by Viboracoustic Sources
SIMI 2016, 2016
Economic development brings environmental concerns in general and in particular regarding the protection against the aggression of external factors on humans, among which noise and vibration. Noise is usually defined as a sound or the amount of unwanted sounds, a by-product of daily activity. Environment vibrations that human body is exposed to may have different causes: construction machinery, heavy equipment and electric hand tools. Whole human body vibrations usually occur during transport, but can be found in other places in the course of industrial processes. Regardless of industrial activity that cause them, vibrations acts on the human body and this can cause discomfort to the operator. Wear of the components of industrial devices influences both noise and mechanical vibration levels. The paper presents correlations occurring between noise and mechanical vibrations emitted by the engine of a car depending on the number of engine revs. For a broader characterization of emission, acoustic power and directivity factor are calculated.
Perceptive analysis of bearing defects (Contribution to vibration monitoring)
Applied Acoustics, 2018
The objective of this article is to improve bearing monitoring via vibration indicators, by developing combinations of indicators, which is more effective than using isolated classical indicators. We use a new perceptive approach to seek correlations between vibration analysis and the perception of noise generated by the defects of rotating machines. Therefore, an experimental bench was developed to simulate machine defects (bearings, alignments and gears) and to perform vibration and acoustic measurements. This study was split into three stages: the first was to process the signals of simulated defects using different vibration analysis techniques (scalar indicators and spectral analysis). The second phase was devoted to a perception test of the noises generated by the simulated defects; this test was carried out on different auditors. The results obtained from the perception test were then processed using multidimensional analysis (MDS), to calculate the dimensions of the sound perception space. The final stage of this study consisted in searching the dimensions correctly representing the deterioration of the simulated defects, and in correlating these subjective dimensions with objective classical indicators (RMS, Kurtosis, Crest factor, CGS, etc.), to define more sensitive indicators calculated from combinations of classical vibration indicators.
The gear whine noise and vibro-acoustic emission of gear-box
Proceedings of the 11th Wseas International Conference on Robotics Control and Manufacturing Technology and 11th Wseas International Conference on Multimedia Systems Signal Processing, 2011
This article reports the results of several tests, carried out in optical gear whine noise to evaluate the difference between the vibro-acoustic emission of two gearboxes of the same type, one, in particular, showing a micro-geometrical error located on the side of tooth and characterized by lack of material at the base of the tooth due to a slight drift of the production process. The tests were carried out in fourth gear. The user of the vehicle, in fact, seems to be particularly attentive to the vibro-acoustic emissions. For that reason the major car manufacturers devote significant resources to the Noise, Vibration and Harshness sector, which deals with noise and vibration of the vehicle to the vehicle driving comfort.
Identification of Noise Emission in a Gear Unit
The Journal of the Acoustical Society of America, 2008
Today it is very important to ensure a stable production without unscheduled outages. To achieve this objective it is required to use advanced production technologies, to ensure adequate maintenance of mechanical systems and to monitor the condition of a device or machine. Reliable and accurate operation of machines and devices with as few outages as possible is desired. The significance of a life cycle design of machines and devices is growing. Possible damages in gear units can be defined by means of monitoring acoustic emission. A crack in the tooth root is usually indicated by significant changes in tooth stiffness. A difference in dynamic responses of an undamaged gear and of a damaged gear can be noted. The possibility of the use of an acoustic method in the field of condition diagnostics is dealt with. The noises produced by a gear unit have been analysed, the noise sources within a gear unit have been determined and the corresponding time-frequency analysis of these sources have been performed, using an acoustic camera.
The purpose of this study is to provide a systematic approach to address the industrial challenge of recognizing impeccable and defective gears by analyzing the spectrum of sound waves produced by the gears when in operation if attached to a testing circuit during sorting. This work classifies the gears into two classes, impeccable and defective classes. The spectra from several samples from both impeccable and defective gears were analyzed and five audio features were extracted from their spectra namely short-time energy, zero-crossing rate, Spectral entropy, pitch, and block energy entropy. It was found that there is a significant difference between the two classes. In training the algorithm, 5D features vectors from 20 feature vectors from impeccable gears and another 5D features vector from 20 defective gears as training samples to determine the discriminating point. In testing the algorithm, 20 samples were extracted randomly from the impeccable and defective gears but whose status was clearly known by visual inspection. The results of gear status given by the algorithm were compared to that of visual inspection. The samples were classified by using the Support Vector Machine (SVM) learning classification approach. A promising efficiency of 95% was obtained.
A Detection of the Quality of sound Gear based on Digital Signal Processing techniques
Despite the fact that products in the industries are made by the same formula, the imperfections and dissimilarities between the produced products are influenced by noises and mechanical connections, moreover the variations of temperature with time during the production contribute to these imperfections. Previously and even to date the produced sound gears in many industries were tested by using the human ears to determine the defect gears and good gears, other methods like pitch detection method and faults detection method are applied. However those method have been found to be unreliable and therefore accelerated to bad sorting between good gears and bad gears. In this paper we proposed a powerful approach which used Digital signal processing techniques to determine the quality of the sound Gears. We integrated Correlation technique, Normalization and alignment methods. The techniques were applied to the extracted sound signals. The final extracted features (amplitudes, frequencies and phases) were classified and hence used to determine the good (non-defect) and bad (defect) gears. The entire process provided the perfect and higher accurate results and suggest that the tool can be used to identify good and bad sound gears.