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Gears are paramout rotary mechanical equipment as its used in many industrial applications, for example in cars, industrial compressors and other applications. Therefore, monitoring the development of its condition is very important to prevent the aggravation of defects and stopping production. Friction between gears is causes of vibration but its representation in modeling and the analysis of its effect on the dynamic response is a very complex matter. In this study, the friction effect was studied by relying on equal load sharing formula and by relying on the sliding velocity direction of single-stage spur gears. The time domain was converted to the frequency domain, depending on the Fast Fourier Transform ( FFT ) method. Dynamic modeling results indicate that the friction between gears has a significant effect on the Vibration response of gearbox. This effect can be noticed by increasing the vibration amplitude in the time domain. It can also be seen by increasing the gear mesh frequency (GMF) amplitude and by increasing the amplitude of its harmonics in the frequency domain.

This study presents a cohesive zone model combining mechanical and thermal effects. Thermal stress was added to the Helmholtz free energy density in order to derive a new approach to incremental damage which included the effect of temperature. The developed damage model has been implemented in ABAQUS using the UMAT subroutine and applied of two different specimens; a three-point bending specimen and a Double Cantilever Beam. The effectiveness of the new method was tested for the given specimens at different temperatures. The simulation results revealed that the total energy of the interface element of high strength carbon fiber reinforced plastic increased as its temperature decreased. It is demonstrated that the load-displacement curves obtained from the numerical model for both test specimens were in good agreement with experimental data available in literature.

Gearbox is an important power transmission component in a wide range of industrial applications. Wear is a common failure mode induced between the contact surfaces of the engaged gear teeth, which result in higher vibration and noise in the gearbox. The early detection of wear is very important for reliable working and better operational performance. Monitoring of gear vibration signature can develop early detection and accurate diagnosis of gear wear fault, and thereby extend the life span of the transmission system. This study focuses on applying an early detection of gear wear that was naturally produced in industrial gearbox system. An experiment gearbox test comprises of back to back two-stage helical gearboxes has been used, where vibration signal was measured and analysed to monitor the health condition of the attached gearboxes under different operation circumstances. Time synchronous averaging (TSA) and wavelet bicoherance (WBC) methods have been used to identify the nonlinearity effect due to induced wear between the contact tooth surfaces. The used methods have the capability to characterize the nonstationary signal and detect the wear at early stages.

This study considers the intra-laminar damage mode in composite structures and its effect on delamination prediction. The progressive damage models for matrix cracking and fibre failure in ABAQUS, based on Hashin's model, are only available for shell elements. The results presented here show that the predicted matrix cracking based on the damage model presently available in ABAQUS diverges from experimental results. A new model based on strain failure criteria, which can be used with both shell elements and 3D solid elements, has been developed. The effect of friction coefficient and enhancement factor on the delamination lobes within the delamination area was investigated, and it is shown that the intact zone can be captured in laminate [03/903]s and [903/03]s subjected to low-velocity impact, by using an enhancement factor of η = 0.75, and friction coefficient [Formula: see text], together with the new model proposed here.

© The Author(s) 2020. A cohesive zone model for delamination propagation in laminated composites under static and fatigue loading has been derived and validated with experimental data under different mode conditions. This study presents a new approach to quantify fatigue delamination degradation based on damage mechanics to evaluate the rate of fatigue damage ((Formula presented.)). The static damage evaluation and fatigue damage degradation are derived from damage surface concept. Both static and fatigue damage linked each other to establish fatigue crack growth formula in the laminated composites. A user-defined subroutine, UMAT, has been employed to develop and implement a damage model in ABAQUS. Two different specimens; a double cantilever beam and a single lap joint were used to investigate the effectiveness of the new method. The simulation results revealed that the developed model had good agreement with experimental data available in literature.

Vibration signals measured from a gearbox are complex multi-component signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures and a substantial amount of noise. This article presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is developed and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio); which make meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a pair of healthy gears and one pair suffering from a tooth breakage with severity fault 1 (25% tooth removal), and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear fault features. This paper illustrates that the new approach offers a more effective way to detect early faults.

© 2016 Owned by the authors, published by EDP Sciences.This article presents an adaptive threshold computation for varying load detection in gearbox system. This method was applied on the vibration signal which is captured from gearbox housing. The experimental work was carried out using a three-stage gearbox connected with a motor and a generator. The developed adaptive threshold is obtained through several repeated experiments in the healthy mode and under the same operating conditions. An appropriate statistical test is used to examine the validity of the adaptive threshold estimation approach. Besides and for all experiments, a confidence interval is obtained which is closely linked to the distribution frequencies of the gearbox vibration signal as a random variable. In addition, several significance levels are considered to show the performances of the proposed adaptive thresholding technique compared to the limitations of the fixed threshold through the rate of false detection alarms. It is demonstrated from various experimental varying load mode of a gearbox system the effectiveness and accuracy of the adaptive threshold in term of evaluating gearbox operating conditions.

Rotating machinery such as induction motors and gears driven by shafts are widely used in industry. A variety of techniques have been employed over the past several decades for fault detection and identification in such machinery. However, there is no universally accepted set of practices with comprehensive diagnostic capabilities. This paper presents a new and sensitive approach, to detect faults in rotating machines; based on principal component techniques and residual matrix analysis (PCRMA) of the vibration measured signals. The residual matrix for machinery vibration is extracted using the PCA method, crest factors of this residual matrix is determined and then machinery condition is assessed based on comparing the crest factor amplitude with the base line (healthy) level. PCRMA method has been applied to vibration data sets collected from several kinds of rotating machinery: a wind turbine, a gearbox, and an induction motor. This approach successfully differentiated the signals from healthy system and systems containing gear tooth breakage, cracks in a turbine blade, and phase imbalance in induction motor currents. The achieved results show that the developed method is found very promising and Crest Factors levels were found very sensitive for machinery condition.

This article presents a comparison between empirical mode decomposition (EMD) and smoothed pseudo-Wigner–Ville distribution (SPWVD) methods based on vibration signature and energy calculation procedure for monitoring gearbox systems. The simulation and experimental work were carried out using two-stage helical gears for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25 per cent tooth removal), fault 2 (50 per cent tooth removal), fault 3 (75 per cent tooth removal), and fault 4 (100 per cent tooth removal) under loads (0, 20, 40, 60, and 80 per cent of the total load) and speeds (10, 30, 50, 70, and 90 per cent of full speed). This article illustrates that the calculation of energy using EMD techniques offers a more effective way to detect early faults than that computations using the SPWVD method, and that the computation of energy using the EMD technique is faster than the calculations done using the SPWVD method.

Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

This paper is concerned with accurate, early, and reliable fault diagnosis using an enhanced vibration measurement technique based on short-time Fourier transform. The novelty of this work lies in detecting very low-phase imbalance-related faults. The energy contained within specified frequency bands centred on the rotor frequency and power supply frequency, and their sideband zones were calculated. The technique was firstly demonstrated by simulated signals and then verified by experimental measurements taken from two different-sized test rigs. The first one comprised a 1.1 kW variable speed three-phase induction motor with varying output load (no load, 25%, 50%, 75%, and 100% load). Two types of common faults were introduced: imbalance in one phase as the electrical fault and misalignment of load as the mechanical fault. The second test rig had a 3 kW three-phase induction motor again with varying load, and here the two seeded faults were: phase imbalance and one broken rotor bar. The measured energy levels in the test conditions were found to be affected by type of fault and fault severity. It is concluded that the proposed method offers a potentially reliable and computationally inexpensive condition monitoring tool which can be implemented with real-time monitoring systems.