AMIYA RANJAN MOHANTY

@iitkgp.ac.in

Professor of Mechanical Engineering
Indian Institute of Technology Kharagpur



              

https://researchid.co/amohanty

RESEARCH INTERESTS

Noise Control, Acoustics of Natural Materials,Signal Proccessing, NVH,Machinery Condition Monitoring, Motor Current Signature Analysis, Underwater Acoustics

133

Scopus Publications

Scopus Publications

  • Improved Time-Varying Tooth Stiffness Calculation in Cracked Spur Gear Using Modified Limiting Line
    Satyajit Mahapatra and Amiya Ranjan Mohanty
    Springer Science and Business Media LLC

  • Dump truck activity recognition using vibration signal and convolutional neural network
    Nagesh Dewangan, Amiya Ranjan Mohanty, and Ranjan Kumar
    Elsevier BV


  • A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities
    Rismaya Kumar Mishra, Anurag Choudhary, S. Fatima, A.R. Mohanty, and B.K. Panigrahi
    Elsevier BV

  • Resonance and Bragg band frequencies coupling of lossy sonic crystals based on nonuniform resonators and lattice
    Debasish Panda and Amiya Ranjan Mohanty
    World Scientific Pub Co Pte Ltd
    Traditional sonic crystals (SCs) are based on scatterers or resonators distributed periodically that attenuate sound waves in different bands of frequencies. Recently, we adopted an improved design of SC, namely, the gradient-based sonic crystal (GBSC), designed with a gradient in the geometric parameters using nonuniform resonators and lattices. The finite element (FE) study on the GBSCs indicated that the gradient induces better attenuation than the traditional periodic SCs. This work is an extension of our previous work with experimental and new FE studies on different parameters of the GBSC in an attempt to improve the attenuation of the GBSCs. It was found that the gradient of the geometry enhances the Bragg scattering and resonance in the array, creating a large number of band frequencies. When designed properly by manipulating their gradient, GBSCs were found to target particular frequency bands over other GBSCs of identical filling ratios by coupling the resonant frequencies or the resonant and Bragg frequencies. It was also found that the thickness of the GBSC can be reduced by removing some specific columns from the array without significantly affecting the attenuation by the GBSC. Similarly, it was also found that the relative position of the resonator columns in a GBSC affects the band frequencies, and swapping the columns may also improve the attenuation by the GBSC.

  • Low-Frequency Wideband Sound Absorption Properties of Composite Layer Micro-perforated Panel Absorber
    D. K. Agarwalla and A. R. Mohanty
    Springer Science and Business Media LLC

  • Multi-fault Diagnosis of Rotating Machine Under Uncertain Speed Conditions
    R. K. Mishra, Anurag Choudhary, S. Fatima, A. R. Mohanty, and B. K. Panigrahi
    Springer Science and Business Media LLC

  • ENHANCING SOUND ABSORPTION OF CURVATURE DAMPED MEMBRANE WITH HETEROGENOUS MICROPERFORATED PANEL
    Raja Kumar and Amiya Ranjan Mohanty
    American Society of Mechanical Engineers
    Abstract Integrating a membrane layer atop microperforated panels (MPP) prevents dust accumulation within the pores, thus preserving their acoustic performance. The induced tension and curvature-induced damping of the membrane on the fixed rim affect the sound absorption. The sound absorption caused by the MPP mostly results from thermal and viscous losses occurring in an array of micrometer-sized holes. The objective of this study is to develop a multilayer sound absorber with a damped membrane coupled with a heterogenous microperforated panel using the two-point impedance with a graph theory-based approach. Further, the effect of the influencing parameters of the membrane, i.e., tension, surface density, damping coefficient of the membrane, and hole diameter, thickness, and perforation porosity of the MPP, is studied. The parametric studies showed how to select the design for a particular frequency to target. The findings demonstrated that including the membrane significantly improved the acoustic characteristics. The presence of air cavities between the membrane and MPP, as well as between MPP and the rigid wall, determines the extent to which the sound absorption peak is shifted.

  • MONITORING MINING DUMP TRUCK OPERATIONS USING CONVOLUTIONAL NEURAL NETWORKS
    Nagesh Dewangan and Amiya Ranjan Mohanty
    American Society of Mechanical Engineers
    Abstract Mining dump trucks are heavy-duty equipment often used for tasks such as material transportation. Coal production at mining sites relies heavily on the effective utilization of dump trucks, specifically designed to carry out a range of tasks crucial for the mining industry. Therefore, monitoring dump truck operations plays an important role in maximizing the overall production of any coal mine. Moreover, the complexity of operations performed by dump trucks necessitates effective monitoring and detection using suitable tools. This study focuses on enhancing monitoring tasks by leveraging image classification algorithms specifically trained to identify operations related to dump trucks. A deep learning-based monitoring approach was developed for monitoring areas, such as identifying transportation-related operations in open-cast coal mines carried out by dump trucks. The study employed vibration signal and its Power Spectral Density images classification to identify dump truck operations. Various established architectures of Convolutional Neural Network were implemented in the approach, and their performances were systematically evaluated. The study has investigated AlexNet, DenseNet121, EfficientNetV2, InceptionResNetV2, ResNet50, and Xception for dump truck operation monitoring. Moreover, the study explored the effectiveness of the models through a case study of vibration measurements from two dump trucks.

  • OPTIMISED DESIGN OF ACOUSTIC BLACK HOLE FOR BROADBAND NOISE ATTENUATION IN DUCTS
    Biren Kumar Pradhan and Amiya Ranjan Mohanty
    American Society of Mechanical Engineers
    Abstract Broadband noise attenuation remains a challenging problem in ducts. The development of acoustic metamaterials in recent times has made it possible to control and manipulate sound waves in novel ways. Acoustic black holes (ABH) are passive type absorbers. The two major parameters affecting the sound absorption characteristics of ABH are known as power-law decay of duct radius and tailored wall admittance. In theory, the tailored geometry induces power-law decay of wave propagation velocity, and the incident wave gets trapped inside the geometry without any reflections. The current study aims to develop an optimised ABH design targeting broadband frequency. The transfer matrix method (TMM) approach is used to characterise the reflection coefficient and validated using numerical approach considering thermoviscous effect along the thin walls. The developed basic design is further optimised for the influencing parameters and validated using numerical analysis. The result shows a broadband noise attenuation for the optimised design of the black hole.

  • A NOVEL DESIGN FOR ENHANCING SOUND QUALITY OF DOMESTIC MIXER GRINDERS





  • Broadband Sound Absorption Technique Using Micro-perforated Panel Absorber with Perforated Extended Panel
    Deepak Kumar Agarwalla and Amiya Ranjan Mohanty
    Springer Science and Business Media LLC

  • A Fault Diagnosis Approach Based on 2D-Vibration Imaging for Bearing Faults
    R. K. Mishra, Anurag Choudhary, S. Fatima, A. R. Mohanty, and B. K. Panigrahi
    Springer Science and Business Media LLC

  • Estimation of Torque Variation due to Torsional Vibration in a Rotating System Using a Kalman Filter-Based Approach
    Satyajit Mahapatra, Akash Shrivastava, Biswajit Sahoo, and Amiya Ranjan Mohanty
    Springer Science and Business Media LLC

  • The Influence of Cladded Resonators on Gradient-Based Sonic Crystals over the Traditional Sonic Crystals
    Debasish Panda and Amiya Ranjan Mohanty
    Springer Science and Business Media LLC

  • Realistic Condition-Based Anomaly Detection of Multi-Faults in Rotating Machines
    Rismaya Kumar Mishra, Anurag Choudhary, Shahab Fatima, Amiya Ranjan Mohanty, and Bijiaya Ketan Panigrahi
    IEEE
    Faulty machine components often get replaced instead of repaired in industries. The involved cost and downtime increase due to this problem, which can be mitigated with proper maintenance planning. This paper proposes a multi-fault diagnosis of two critical rotating components, motor and bearing at a system level. Motor current signatures of all possible muti-fault conditions were acquired at different speeds. The extracted signals were divided into small signal segments by implementing a sliding window with a fixed sample size. Continuous Wavelet Transform (CWT) was then employed on each extracted segment, and different datasets were formed for doing signal, sequence and image classification using Artificial Neural Network (ANN), 1D Convolutional Neural Network (CNN) and 2D CNN models, respectively. A comparative analysis of model performance was performed and found that 2D CNN outperformed other machine learning models. The Motor Current Signature Analysis (MCSA) could be used for system-level multi-fault anomaly detection using machine learning algorithms.

  • REMAINING USEFUL LIFE PREDICTION OF BEARINGS BASED ON COX HAZARD MODEL



  • A self-adaptive multiple-fault diagnosis system for rolling element bearings
    R K Mishra, Anurag Choudhary, S Fatima, A R Mohanty, and B K Panigrahi
    IOP Publishing
    Abstract The inevitable simultaneous formation of multiple-faults in bearings generates severe vibrations, causing premature component failure and unnecessary downtime. For accurate diagnosis of multiple-faults, machine learning (ML) models need to be trained with the signature of different multiple-faults, which increases the data acquisition time and expense. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single-fault vibration signatures. A time-frequency-based hybrid signal processing technique, which involves discrete wavelet transform and Hilbert transform, was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimized metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The results show that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination using single-fault data. This will be helpful where accessibility to large amounts of data is limited for multiple-fault diagnosis.

  • An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization
    Rismaya Kumar Mishra, Anurag Choudhary, AR Mohanty, and S Fatima
    SAGE Publications
    Bearing is regarded as one of the core elements in rotating machines and its fault diagnosis is essential for better reliability and availability of the rotating machines. This paper puts forward an intelligent vibration signal-based fault diagnosis approach for bearing faults identification at an early stage, irrespective of speed conditions. The proposed methodology comprises of a frequency shift-based hybrid signal processing technique that involves a combination of Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) followed by sliding window-based feature extraction. Thereafter, a newly developed Henry Gas Solubility Optimization (HGSO) is implemented to select the relevant features. At last, the optimal attributes are used to train the Artificial Neural Network (ANN) model for the classification of the different bearing faults. To test the effectiveness of the speed independent model, experimental validation was done with constant and varying speed conditions. The results demonstrate that the proposed methodology has a tremendous potential to eliminate unplanned failures caused by bearing in rotating machinery.


  • Acoustical Study of a Lossy Gradient-Based Sonic Crystal Using Acoustic Beamforming
    Debasish Panda and Amiya Ranjan Mohanty
    World Scientific Pub Co Pte Ltd
    Sonic crystals (SCs) are unique periodic structures designed to attenuate acoustic waves in tunable frequency bands known as bandgaps. Though previous works on conventional uniform SCs show good insertion loss (IL) inside the bandgaps, this work is focused on widening their bandgaps and achieving better IL inside the bandgaps by using a gradient-based sonic crystal (GBSC). The GBSC applies property gradient to the conventional SC array by varying its basic properties, i.e., the distance between the scatterers/resonators (lattice constant), and resonator dimensions between the columns and hence the name GBSC. The design of the GBSC is backed by the results of acoustic beamforming experiments conducted over the uniform SCs of hollow scatterers and Helmholtz resonators (HRs) having two-dimensional (2D) periodicity prepared by using Polyvinyl chloride (PVC) pipes without any property gradient and their respective 2D finite element (FE) studies. The experimental and FE simulation results of the uniform SCs were found to be in good agreement and therefore, the GBSC was modeled and analyzed using FE method considering the viscothermal losses inside the resonators. The results indicated that the property gradient improves both Bragg scattering and Helmholtz resonance compared to that of the uniform SCs and therefore, the GBSC exhibits wider attenuation gaps and higher attenuation levels. An array of 30 microphones was used to conduct acoustic beamforming experiments on the uniform SCs. Beamforming was found to be an advanced and fast method to perform quick measurements on the SCs.