Bevel Gearbox Research Articles

The Evaluating Method for Module Partition Schemes of Complex Product Systems

Module partition of Complex product system (CoPS) usually produces a number of schemes, and how to choose the best one amongst the themes is very important. In this research, according to the features of CoPS, the multi-level evaluation indexes are built based on the DFX (design for X) method, which can evaluate the scheme comprehensively. The weight of indexes are obtained with the method of analytical network process. Then, the multi-level grey fuzzy comprehensive evaluation method is proposed to make the well-chosen decision based on the DFX multi-objective evaluation model. The evaluation method are illustrated using the partition schemes of the experiment platform of a bevel gearbox.

Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions

Signal processing is an important tool for diagnostics of mechanical systems. Many different techniques are available to process experimental signals, among others: FFT, wavelet transform, cepstrum, demodulation analysis, second order ciclostationarity analysis, etc. However, often hypothesis about data and computational efforts restrict the application of some techniques. In order to overcome these limitations, the empirical mode decomposition has been proposed. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert–Huang spectrum. Anyhow, the selection of the intrinsic mode functions used for the calculation of Hilbert–Huang spectrum is normally done on the basis of user’s experience. On the contrary, in the paper a merit index is introduced that allows the automatic selection of the intrinsic mode functions that should be used. The effectiveness of the improvement is proven by the result of the experimental tests presented and performed on a test-rig equipped with a spiral bevel gearbox, whose high contact ratio made difficult to diagnose also serious damages of the gears. This kind of gearbox is normally never employed for benchmarking diagnostics techniques. By using the merit index, the defective gearbox is always univocally identified, also considering transient operating conditions.

Teager energy operator for multi-modulation extraction and its application for gearboxfault detection**US patent pending (serial number: 12/631,528).

This paper presents a parameter-free and broadband approach to detecting gear faultsbased on vibration signals. The technique is implemented using the Teager energy operator(TEO). It is shown that this operator can extract amplitude, phase and frequencymodulations that are associated with various gear faults. Spectral analysis of theTEO-transformed signal provides the necessary information for fault detection. To improvethe effectiveness of the proposed technique, we also devised a wavelet de-noising step basedon online threshold estimation. In the de-noising step, the threshold estimation isperformed through a frequency domain median absolute deviation (FMAD) scheme. Theproposed fault detection technique is tested on simulated as well as experimentaldata acquired from a single-stage bevel gearbox and a two-stage parallel gearbox.

Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)

An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive processes are caused for gear box, they began its deterioration from early stages, also called incipient level. The extracted features from the DWT are used as inputs in a neural network for classification purposes. The results show that the developed method can reliably diagnose different conditions of the gear box. The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This paper investigates the use of discrete wavelets for feature extraction and artificial neural network for classification.

Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)

Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or assembly under study. This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gear box using artificial neural network (ANN) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.

A case study on classification of features by fast single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box

This paper deals with the application of fast single-shot multiclass proximal support vector machine for fault diagnosis of a gear box consisting of twenty four classes. The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing multiclass proximal support vector machine. The efficiency and time consumption in classifying the twenty four classes all-at-once is reported.

Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification

The wavelet transform (WT) is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In present work, a discrete wavelet, Daubechies wavelets (db1–db15) is used for feature extraction and their relative effectiveness in feature extraction is compared. The major steps in pattern classification are feature extraction and classification. This paper investigates the use of discrete wavelets for feature extraction and a Decision Tree for classification. J48 Decision Tree algorithm has been used for feature selection as well as for classification. This paper illustrates the powerfulness and flexibility of the discrete wavelet transform to decompose linear and non-linear processing of vibration signal.

Experimental method of tooth contact analysis (TCA) with rapid prototyping (RP) use

PurposeThe purpose of this paper is to determine the instantaneous contact area in a gearbox using rapid prototyping.Design/methodology/approachThe determination of the contact area utilizes one of the RP techniques, i.e. stereolithography. Stereolithography enables the making of gears with complex profiles, which are difficult to obtain by means of other machining methods. A model of a bevel gearbox with Gleason spiral generated modify roll (Gleason SGM) gears with circular‐arc profiles of teeth is applied as an example for testing. A prototype of the gearbox was made using SLA 250 apparatus.FindingsCorrect mating of gears and their kinematical precision depend on the shape and size of the instantaneous contact area, as well as changes during the turning of gears. Contact between gear surfaces of a geometrically ideal gearbox occurs at a point or line, but, because of a deformability of mating flanks teeth, in reality it is always a certain surface. This paper presents research on the instantaneous contact area with area on the surface of a flank tooth, which is in contact with the mating surface of another tooth at a specific moment.Originality/valueThe described method in the paper enables a dynamic determination of the mating area gearbox. Existing experimental methods enable only a static observation of the mating area. A stand test was built and enables an exact meshing of mating gears. Gears were made of transparent material, SL‐5170, which enables observation of the instantaneous contact area.

Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique

To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. The vibration signal acquired from the operating machine has been used to effectively diagnose the condition of inaccessible moving components inside the machine. Suitable sensors are kept at various locations to pick up the signals produced by machinery and these signals are very meaningful in condition diagnosis surveillance. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing is required. This paper presents the use of decision tree for selecting best statistical features that will discriminate the fault conditions of the gear box from the signals extracted. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions – good bevel gear, bevel gear with tooth breakage (GTB), bevel gear with crack at root of the tooth (GTC), and bevel gear with face wear of the teeth (TFW) for various loading and lubrication conditions. The statistical features were extracted and good features that discriminate the different fault conditions of the gearbox were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.

A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.

Audio acoustic plant condition monitoring of spiral bevel gearbox

Plant condition monitoring (PCM) is widely used by a variety of industries as part of a condition based maintenance programme. This replaces the previous ‘schedule’ based maintenance programme, in which individual components of a machine are replaced at specified intervals. With PCM the condition of the individual components is monitored, and they are only replaced when their performance is deemed unsatisfactory. PCM techniques are often capital and/or labour intensive, and their use limited to critical machines only. The objective of the present research is to develop a system comprised of microphones and accelerometers which is able to screen industrial environments, such as the hot rolling mill at Corus's Port Talbot plant, for machine faults. The system would use the minimum number of transducers to remotely screen the maximum number of machines, but is not required to provide detailed diagnostic information. To date, an omnidirectional microphone has been used successfully to detect badly damaged gear teeth. The method does not require the use of a soundproof enclosure to filter out background noise. A finite element analysis model of the test rig has been created to determine the eigenfrequencies and eigenmodes of the test rig and to improve the understanding of the vibration behaviour of the faults.

Design and development of a powered vertical disc plough

A PTO operated vertical disc plough with four number of discs of 560 mm diameter was designed and developed for tillage operation in rice harvested fields. The power from tractor PTO to the disc gang was transmitted through a 60° bevel gear box and chain-sprocket drive. The width of cut was about 820 mm. The performance was evaluated in rice harvested field for different levels of disc to ground velocity ratio and depth of operation for a peripheral disc velocity of 158.3 m/s. The fuel consumption was found to increase with A and depth of operation. The gross specific fuel consumption was lower for a A value of 2 while the draft requirement and wheel slippage were found less. The clod size was found smaller with higher percentage of stubble inversion. It was concluded that a large size of equipment, in powered mode, could be used for same size of tractor for better power utilization.

Torvac Passes the Test

PLESSEY Aerospace at Titchfield, Hants has installed a Compact 100 vacuum furnace from Cambridge‐based suppliers Torvac. The furnace has just completed the warranty year in operation on a wide range of heat treatments, brazing and degassing applications. The work is generally carried out to the full rigors of aerospace standards including mSR 953, CAA DAI/7591/65 and STAN 05/21. Typical of the jobbing work is the vacuum hardening of bevel gear box components which Plessey manufacture for the AV8B engines used in the Harrier jump jet.