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Highlights • Time delay estimated by EMD ML in low SNR and reverberant underwater environment. • In EMD ML method, estimation of impulse response function is been avoided. • Among TDE methods EMD ML is suitable in low signal strength, low frequency source. Abstract In recent times, time delay estimation (TDE) has received significant practical importance in sonar, radar, GPS, and various other fields. In a passive sonar system, estimation of time delay for low frequency and low signal to noise (SNR) acoustic source is a difficult task. If the source and receivers are kept inside the reverberation environment, time delay estimation becomes more difficult because of sound source echo's. This research work proposes a new TDE approach named empirical mode decomposition maximum likelihood time delay estimation (EMD ML TDE) method, for the low-frequency and low SNR underwater machinery acoustic signal in a reverberant environment. EMD ML TDE method is based on maximum likelihood (ML) method, and it is using empirical mode decomposition (EMD) denoising technique to estimate acoustic sound signal and noise from the noisy reverberant signal. The experimental results are provided that this new approach is better to estimate time delay in reverberant environments. [ABSTRACT FROM AUTHOR]

Heating, Ventilation and Air Conditioning (HVAC) unit is a major noise source in a vehicle’s interior space. Reducing this noise will improve the sound quality of the vehicle’s interior space and enhance passengers’ experience. However, current noise control techniques are high-cost and hazardous to environment. Therefore, this paper studies the first ever usage of low-cost, biodegradable natural materials for vehicle’s HVAC noise control. Jute felt and waste cotton were found to have higher sound absorption coefficients than other common sound absorbing natural materials, and were chosen as sound absorbers for noise control treatment of a prototype HVAC unit. Noise sources in the unit were identified and ranked, and the treatment was applied to these. The treatment significantly reduced the noise level (by 4 dBA) and loudness level (by 7 sones) due to the unit at the reference passenger’s ear location, with negligible cost and weight. Sound quality evaluation by 24 participants showed that the treatment significantly reduced the annoyance of the vehicle interior soundscape comprising the HVAC noise. Thus, jute felt and waste cotton are low-cost, light-weight, biodegradable and recyclable natural materials with high potential for HVAC noise control. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise. [ABSTRACT FROM AUTHOR]

Many industrial processes require knowledge of the level of hot bubbling liquid in a closed vessel. However, usually conventional instrumentation is unsuitable because of extremely high temperatures, and hot poisonous gases generated by the thermo-chemical processes of the boiling liquid. This paper proposes a novel method to detect such boiling liquid levels by monitoring the boiling/bubbling noise in the vessel using a waveguide. The principle of this method is that the axial modes of the air column and the liquid column in the closed vessel would change with change in liquid height. The sound pressure waves produced in the vessel due to bubbling and propagating along the vessel’s axial direction can be captured by decomposition using two microphones in a waveguide co-axially attached to the vessel. These decomposed waves would show peaks at resonance frequencies corresponding to the vessel’s axial modes, which would be used to calculate the liquid height. The proposed method was verified through extensive finite element simulations and experiments of boiling water over a wide range of conditions. The boiling water levels were correctly measured in each condition with an average accuracy of 98.8%. Thus, this waveguide system can continuously monitor boiling liquid levels based on the incident wave frequency and amplitude. Such a system has wide industrial applications, particularly in steel plants, where knowing the amount of molten steel during oxygen lancing faces many challenges. [ABSTRACT FROM AUTHOR]

Here, the large amplitude axisymmetric vibration of a circular plate having a circumferential crack is studied considering the simply supported and clamped boundary conditions. Exact modal functions are generated by satisfying the boundary conditions and constraints along the crack. The inplane or membrane forces resulting from the large deflection are derived from the Berger's formulation. Next, the Galerkin's method is used to transform the differential equation into the Duffing equation with cubic nonlinearities. The amplitude and phase curves are generated using the Method of multiple scales. Natural frequency variations of axisymmetric modes are studied for a change in crack depth and crack position, considering both boundary conditions. Some natural frequencies are also compared with the finite element results and they are found to be in good agreement. For clamped boundary conditions, the natural frequency of a cracked plate is shown to approach that of an uncracked plate for a particular crack position independent of different crack depths. The amplitude and phase curves are shown for different crack parameters. Finally, the phase plane plot variations are presented for different crack depths, positions and boundary conditions. [ABSTRACT FROM AUTHOR]

Detection of spark ignition engine firing is an important parameter to understand proper and improper combustion inside the engine cylinder. The engine firing can be detected by direct measurement of in-cylinder pressure by using piezo-electric sensor. However, the cost of the sensor is really high. Therefore, in this paper, engine firing has been detected by analysis of instantaneous angular speed (IAS) signal from output shaft measured by cheap device like rotary encoder. The direct short time Fourier transform (STFT) is unable to detect engine firing clearly as the signal contains various frequencies (rotational frequency and its harmonics) other than engine firing frequency. Therefore, an algorithm has been developed based on complementary ensemble empirical mode decomposition (CEEMD) and followed by STFT and applied on IAS signal to detect engine firing. Moreover, a comparative study has been performed between laser vibrometer signal and IAS signal to understand the efficacy of these signals. [ABSTRACT FROM AUTHOR]

For modeling of jute as acoustic material, knowledge of its non-acoustical parameters like porosity, tortuosity, air flow resistivity, thermal and viscous characteristic lengths is a prime requisite. Measurement of these non-acoustical parameters is not straightforward and involves a dedicated measurement setup. So in order to overcome this issue, the inverse acoustical characterization can be used. In this paper, the particle swarm optimization method (PSO) is used as an optimization method. This method estimates the non-acoustical parameters of jute material in felt form by minimizing the error between experimental and theoretical sound absorption data. In this work, the impedance prediction models for fibrous materials like Johnson–Champoux–Allard model with rigid and limp frame and Garai–Pompoli model is used for sound absorption coefficient calculation by the transfer matrix method along with the PSO. The inverse estimated non-acoustical parameters for jute material are then compared with estimated and experimentally measured parameters for jute felts. Using these inversely predicted parameters, sound absorption of multilayer sound absorbers is also studied. [ABSTRACT FROM AUTHOR]

• Explosive sound source localization through experimental and simulation studies. • Significance of reference microphone selection on positioning accuracy. • Optimization of the number of microphones and microphone spacing for sound source localization. • For distant source positioning, microphone spacing should be at least 0.34 times the source range. • Performance study of microphone array geometries using modified Levenberg-Marquardt algorithm. The use of microphone array for sound source localization has been a trending area of research in speech source recognition, battlefield acoustics, etc. For such applications, the localization accuracy is significantly influenced by the number of microphones and their arrangement on the ground surface. Thus, this paper proposes optimized microphone array geometries with the optimized number of microphones for positioning near and distant sound sources. Moreover, this paper also discusses the significance of the reference microphone selection on positioning accuracy. Here, the time-delay-based multilateration approach is used to formulate the governing nonlinear hyperbolic equations for sound source localization. The resulting equations are solved using the least square optimizer, the modified Levenberg Marquardt algorithm. The source results for several customized array structures are validated through simulation and experimental studies. Based on the results, this paper proposes that at least six microphones are required to identify a source position (< 1800 m) with 97 % accuracy at lower noise thresholds (SNR: (6 to 20) dB) and 95 % accuracy at higher noise thresholds (SNR: - 6 to 0 dB). Moreover, the paper suggests that microphone spacing should be a minimum of 0.34 times the source range for localization of distant sound sources to improve accuracy at higher and lower noise levels. [ABSTRACT FROM AUTHOR]

This paper describes the interpretations of underwater noise spectra of rainfall on water bodies. It uses a simple methodology of measurement of bubbles of various sizes up to 10 mm in diameter and the comparison of resonating frequencies of the above bubbles with theoretical calculations. The underwater noise of rainfall is a combined effect of drop impacts and bubble bursting. The rain drop size during actual rainfall varies between 0.8 and 5 mm in diameter and the underwater bubble sizes can vary from 0.5 mm to 10 mm in diameter. The detailed description of the experimental results of the underwater spectrum of natural light rain and artificially generated rain are presented. By analyzing the resonating frequencies of underwater bubbles for different type of rainfall, it is plausible to interpret the underwater noise spectrum of rainfall on water bodies and then predict the amount of rainfall. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, vibration analysis of a rectangular thin isotropic plate with a part-through surface crack of arbitrary orientation and position is performed by using the Kirchhoff plate theory. Simply supported (SSSS), clamped (CCCC) and simply supported–clamped (SCSC) boundary conditions are considered for the analysis. First, the governing differential equation of a cracked plate is formulated. A modified line spring model is then used to formulate the crack terms in the governing equation. Next, by the application of Burger's formulation, the differential equation is transformed into the well-known Duffing equation with cubic and quadratic nonlinearities. The Duffing equation is then solved by the method of multiple scales (MMS) to extract the frequency response curve. Natural frequencies are evaluated for different values of length, angle and position of a part-through surface crack. Some results are compared with the published literature. Amplitude variation with different values of length, angle and position of a part-through surface crack are presented, for all three types of the plate boundary conditions. [Copyright &y& Elsevier]

• Explosive sound source positioning by using modified Levenberg Marquardt algorithm. • A comparative analysis of different positioning methods. • Modified LMA performance validation through simulation and experimental studies. • A short and wide range of sound source positioning by varying r between 0.002 and 2. • Sound source localization for both indoor and outdoor environments. In this paper, a modified Levenberg-Marquardt algorithm (MLMA) is proposed to localize the 'point of burst' of an explosive sound source over the range of (0.5–2500) m. The objective function for minimization is formulated through the time difference of arrival based multilateration approach. The developed method uses four exclusive steps to satisfy global convergence along with fast computational speed. The performance of the proposed method is validated through both indoor-outdoor experiments and simulation studies and compared to other well-known methods. The experimental results show that the non-iterative approaches perform satisfactorily only if the ratio of microphone spacing to source range r is greater than 0.30. However, the iterative approaches outperform non-iterative approaches for any r. It is also observed that the MLMA converges globally at least five times faster than other algorithms. The numerical simulation results also demonstrate that the MLMA provides optimal solutions at lower and higher noise thresholds. [ABSTRACT FROM AUTHOR]

Abstract: This research aims to study the acoustical and flammability properties of biodegradable and easily disposable natural fibre jute and its composite for noise reduction in house hold appliances, automotive and architectural applications. Acoustical properties of jute fibre and felt (natural rubber latex jute composite) were measured in terms of normal specific sound absorption coefficient and sound transmission loss whereas fire retardant tests included limiting oxygen, flame propagation and smoke density test. The results illustrate that low density jute is a better sound absorber as compared to high density jute material, moreover natural rubber latex jute composite gives higher sound transmission class value than jute felt/cloth. Results were also compared with commercially available synthetic, non-biodegradable, glass fibre which indicates that the noise reduction coefficient value and sound transmission class rating of natural rubber latex jute felt are comparable to that of the popular fibre glass. Fire retardant tests show composite’s high limiting oxygen index value as compared to fibreboard and other natural sound absorbing material, wool, low smoke density rating and low light absorption with respect to fibre glass as well as self fire extinguishing ability. [ABSTRACT FROM AUTHOR]

Abstract: Vibration monitoring is one of the primary techniques of condition monitoring of rotating machines. Shaft misalignment and rotor unbalance are the main sources of vibration in rotating machines. In this study a model based technique for fault diagnosis of rotor–bearing system is described. Using the residual generation technique, residual vibrations are generated from experimental results for the rotor bearing system subject to misalignment and unbalance, and then the residual forces due to presence of faults are calculated. These residual forces are compared with the equivalent theoretical forces due to faults. The fault condition and location of faults are successfully detected by this model based technique. [Copyright &y& Elsevier]

Abstract: This paper deals with an experimental investigation of fault diagnosis in a multistage gearbox under transient loads. An induction motor drives the multistage gearbox, which is connected to a DC generator for loading purpose. The signals studied are the vibration transients, recorded from an accelerometer fitted at the tail-end bearing of the gearbox; and the current transients drawn by the induction motor. Three defective cases and three transient load conditions are investigated. Advanced signal processing techniques such as discrete wavelet transform (DWT) and a corrected multiresolution Fourier transform (MFT) are applied to investigate the vibration and current transients. It is observed from the vibration transients that the load removal is a high-frequency phenomenon. With increase in defect severity, not only the defective gear mesh frequency gains energy, but also large impact energy appears in low-frequency regions. Whereas in the current transients, though load removal is a low-frequency phenomenon, a very small transient is observed at high-frequency regions for defective gears. With increase in defect severity, energy is distributed to the sidebands of the gear mesh frequency across supply line frequency. A statistical feature extraction technique is proposed in order to find a trend in detection of defects. A condition monitoring scheme is devised that can facilitate in monitoring vibration and current transients in the gearbox with simultaneous presence of transient loads and defects. [Copyright &y& Elsevier]

Abstract: This paper deals with determining various time-varying parameters that are instrumental in introducing noise and vibration in a helical gear system. The most important parameter is the contact line variation, which subsequently induces friction force variation, frictional torque variation and variation in the forces at the bearings. The contact line variation will also give rise to gear mesh stiffness and damping variations. All these parameters are simulated for a defect-free and two defective cases of a helical gear system. The defective cases include one tooth missing and two teeth missing in the helical gear. The algorithm formulated in this paper is found to be simple and effective in determining the time-varying parameters. [Copyright &y& Elsevier]

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time–frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert–Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal''s frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD). [Copyright &y& Elsevier]

Abstract: Time varying frictional torque is one of the principal exciting sources of vibration in gears. A number of literature have already been devoted to find an expression for time-varying frictional torque for helical and spur gears. For helical gears, time-varying contact length excites a number of other time-varying parameters such as mesh stiffness, damping coefficient, and frictional force and torque which subsequently give rise to time-varying bending moment and bearing force. Hence an elaborate review of time-varying contact length, and frictional force and torque is covered in this paper. A new approach and algorithm to find frictional torque for investigation of torsional vibration of helical gear system has been suggested. [Copyright &y& Elsevier]

Abstract: Even though there are a number of condition monitoring and analysis techniques, researchers are in search of a simple and easy way to monitor vibration of a gearbox, which is an omnipresent and an important power transmission component in any machinery. Motor current signature analysis (MCSA) has been the most recent addition as a non-intrusive and easy to measure condition monitoring technique. The objective of this paper is to detect artificially introduced defects in gears of a multistage automotive transmission gearbox at different gear operations using MCSA as a condition monitoring technique and Kolmogorov–Smirnov (KS) test as an analysis technique assuming that any defect or load has a specific probability distribution. Empirical cumulative distribution functions (ECDF) are used to differentiate these probability distributions. Steady as well as fluctuating load conditions on the gearbox are tested for both vibration and current signatures during different gear operations. It is concluded that combined MCSA and KS test can be an effective way to monitor and detect faults in gears. [Copyright &y& Elsevier]

Abstract: 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. An efficient and new but non-intrusive method to detect the fluctuation in gear load may be the motor current signature analysis (MCSA). In this paper, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA. It has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected. Hence, discrete wavelet transform (DWT) is suggested to decompose the current signal, and FFT analysis is carried out with the decomposed current signal to trace the sidebands of the high frequencies of vibration. The advantage of DWT technique to study the transients in MCSA has also been cited. The inability of CWT in detecting either defects or load fluctuation has been shown. The results indicate that MCSA along with DWT can be a good replacement for conventional vibration monitoring. [Copyright &y& Elsevier]

Bearing race faults have been detected by using discrete wavelet transform (DWT). Vibration signals from ball bearings having single and multiple point defects on inner race, outer race and the combination faults have been considered for analysis. The impulses in vibration signals due to bearing faults are prominent in wavelet decompositions. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. It has been shown that DWT can be used as an effective tool for detecting single and multiple faults in the ball bearings. [Copyright &y& Elsevier]

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rotating machines is essential for the safe and efficient operation of maritime vessels. It prevents potential failures in rotating machines in maritime systems. Simultaneously developed faults severely damage the machines, leading to unprecedented incidents and higher maintenance costs. Data availability for developing multi-fault diagnosis solutions is limited, creating maintainability problems. The unavailability of data also causes further obstacles in the accurate diagnosis of machines. The efficiency of maritime machinery can be improved with intelligent diagnosis by advanced machine learning algorithms. This paper proposes a methodology to diagnose multi-faults with balanced and imbalanced acoustic, vibration and current signals datasets. After acquiring the data from an experimental setup under various speeds, a time-frequency-based Stepping Frame Synchrosqueezed Fourier Transform (SFSFT) is deployed in each segmented signal prior to feature extraction. The extracted features are used for training the Radial Basis Function Neural Network (RBFNN). Different balanced and imbalanced datasets are created to check the multi-fault classification ability of the proposed methodology. The results show that SFSFT-RBFNN is a generalised methodology, which performed tremendously well with a maximum classification accuracy of 100 % for balanced and imbalanced datasets in all three modality datasets. • Synchrosqueezed Fourier Transform is improved with stepping frame extraction method for multi-fault diagnosis. • Minute details of multi-fault signatures are extracted using SFSFT, and RBFNN is used for fault detection. • Performance of the proposed method is tested with balanced and different imbalanced datasets. • Methodology validation is carried out with acoustic, vibration and current datasets. [ABSTRACT FROM AUTHOR]

The vibrational characteristics of a slant-cracked rotor passing through its flexural critical speed have been analysed by using finite element method for flexural vibrations. The transient response of a cracked rotor has been obtained by applying an unbalance force and a harmonically varying torque on it. When a crack is present in the rotor, the sub and super harmonic frequency components at an interval frequency corresponding to torsional frequency were found to be centered on critical speed of the rotor system. A parametric study has been carried out for transient response of the rotor for different crack depths, rotor angular accelerations and torsional frequencies. Some suggestions for detection and monitoring of the slant cracks in the rotor are described. [Copyright &y& Elsevier]

This paper deals with acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor (LMFBR) based on auto regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium–water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and support vectors machines (SVM). The proposed approach takes into account operating mode information in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l’Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected. [ABSTRACT FROM AUTHOR]

Abstract: Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations. [Copyright &y& Elsevier]