Browsing by Author "Jalan, Arun Kumar"

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Analysis of faults in rotor-bearing system using three-level full factorial design and response surface methodology
(Sage, 2021-07) Jalan, Arun Kumar
This article presents the experimental and statistical methodology for localized fault analysis in the rotor-bearing system. These defects on outer race, on inner race, and on a combination of ball and outer race are considered. In this study speed, load and defects were considered as the essential process variables to understand their significance and effects on vibration response for the rotor-bearing system. Three factors at three levels were considered for experimentation, and the experiment was designed for L27 based on design of experiments (DOE) methodology. From the experiments, the vibration response results are recorded in terms of root mean square value for the analysis. Response surface methodology (RSM) is used for identifying the interaction effect of varying process parameters upon the response of vibrations by response surface plot. The rotor-bearing test setup is used for experimentation and is analyzed by using DOE. This study establishes the prediction of fault in the rotor-bearing system in combined parametric effect analysis and its influence with DOE and RSM.
Classification of Ball Bearing Faults Using Vibro-Acoustic Sensor Data Fusion
(Springer, 2019-04) Jalan, Arun Kumar
This paper presents the novel technique for fault diagnosis of bearing by fusion of two different sensors: Vibration based and acoustic emission-based sensor. The diagnosis process involves the following steps: Data Acquisition and signal processing, Feature extraction, Classification of features, High-level data fusion and Decision making. Experiments are carried out upon test bearings with a fusion of sensors to obtain signals in time domain. Then, signal indicators for each signal have been calculated. Classifier called K-nearest neighbor (KNN) has been used for classification of fault conditions. Then, high-level sensor fusion was carried out to gain useful data for fault classification. The decision-making step allows understanding that vibration-based sensors are helpful in detecting inner race and outer race defect whereas the acoustic-based sensor is more useful for ball defects detection. These studies based on fusion helps to detect all the faults of rolling bearing at an early stage.
Computational analysis of mhd nanofluid flow across a heated square cylinder with heat transfer and entropy generation
(Sciendo, 2024-08) Sharma, Bhupendra Kumar; Jalan, Arun Kumar
The mixed convection heat transfer of nanofluid flow in a heated square cylinder under the influence of a magnetic field is considered in this paper. ANSYS FLUENT computational fluid dynamics (CFD) software with a finite volume approach is used to solve unsteady two-dimensional Navier-Stokes and energy equations. The numerical solutions for velocity, thermal conductivity, temperature, Nusselt number and the effect of the parameters have been obtained; the intensity of the magnetic field, Richardson number, nanoparticle volume fraction, magnetic field parameter and nanoparticle diameter have also been investigated. The results indicate that as the dimensions of nanoparticles decrease, there is an observed augmentation in heat transfer rates from the square cylinder for a fixed volume concentration. This increment in heat transfer rate becomes approximately 2.5%–5% when nanoparticle size decreases from 100 nm to 30 nm for various particle volume fractions. Moreover, the magnitude of the Nusselt number enhances with the increase in magnetic field intensity and has the opposite impact on the Richardson number. The findings of the present study bear substantial implications for diverse applications, particularly in the realm of thermal management systems, where optimising heat transfer is crucial for enhancing the efficiency of electronic devices, cooling systems and other technological advancements.
Condition Monitoring of Misaligned Rotor System Using Acoustic Sensor by Response Surface Methodology
(ASME, 2022) Jalan, Arun Kumar; Marathe, Amol
Misalignment is among the most common causes of vibrations in rotary machinery. Modern machinery is complicated and installing a sensor might be tricky at times. As a result, noncontact type sensors are critical in such situations. The present study investigates the influence of combinations between speed, load, and fault severity upon system vibration by employing acoustic sensor. Although acoustic sensor is used in angular fault diagnosis, however, this is the first attempt to combine the noncontact type of sensor and response surface methodology (RSM) to study the influence of misalignment upon system vibration and the factors that induce system vibrations in a misaligned rotor system. To investigate the effect of these interactions on system performance, RSM with root-mean-square (RMS) as a response factor is used. Design of experiments is used to prepare experiments, while analysis of variance (ANOVA) is used to analyze the results. Speed has a significant impact on RMS value in both parallel and angular types of misalignments and it severely affects the system's performance. According to the RSM findings, a change in load influences vibration amplitude. With increasing defect severity, the change in RMS value was not particularly significant. The outcome of RSM using acoustic sensor was found well aligned with the conclusion drawn using RSM study with vibrational sensor.
Diagnosis and prognosis of oil whirl in journal bearing using acoustical measurement
(ASME, 2025-05) Jalan, Arun Kumar
Clearance and length-to-diameter (L/D) ratio are essential parameters for the optimum journal-bearing design. This article investigates the effect of load and rotational speed of the shaft on those two parameters. Under specific operating conditions, oil whirl is a prevalent rotor dynamic instability that can result in significant machine faults if left unattended. Experiments are carried out at different speed and loading conditions while varying the clearance (0.05 mm and 0.15 mm) and L/D ratio (1.5 and 2) of journal bearings. The data analysis used statistical features, like skewness, kurtosis, and spectral kurtosis, to evaluate the severity of the oil whirl and the system's behavior. Comparative analysis was carried out using these statistical measures to identify the best feature for analyzing oil whirl. The results indicate that the onset and intensity of the oil whirl are strongly influenced by both clearance and L/D ratio, and considerable negative skewness, high kurtosis, and high spectral kurtosis values are seen under different load and speed combinations. This study emphasizes the importance of optimizing both clearance and L/D ratio in bearing design to mitigate oil whirl under various operating conditions.
Ensemble Subspace Discriminant Classifiers for Misalignment Fault Classification Using Vibro-acoustic Sensor Data Fusion
(Springer, 2022-05) Jalan, Arun Kumar
Misalignment is one of the major reasons for rotating machinery breakdown. Conventionally, misalignment diagnosis is done by the occurrence of a strong peak at 2x running speed is widely accepted.
Experimental Investigation Using Response Surface Methodology for Condition Monitoring of Misaligned Rotor System
(ASME, 2021-08) Marathe, Amol; Jalan, Arun Kumar
Misalignment is one of the key reasons for vibrations in most of the rotating system. The present study focuses on interactions among speed, load, and defect severity by investigating their effect on the system vibration. Response surface methodology (RSM) with root-mean-square (RMS) as a response factor is used to understand the influence of such interactions on the system performance. Experiments are planned using design of experiments, and analysis is carried out using analysis of variance (ANOVA). It is observed that speed has a remarkable effect on RMS value in both parallel and angular types of misalignment and affects the system performance. RSM results revealed that a change in load has less impact on vibration amplitude in case of horizontal and vertical directions, but there is a significant variation in RMS value in axial direction for both types of misalignment. A slight increase in the RMS value with an increase in defect severity is observed in the axial direction. These observations will help to understand the misalignment defect and its effect in a better way.
Fault diagnosis of rolling element bearing based on artificial neural network
(Springer, 2019-02) Jalan, Arun Kumar; Belgamwar, Sachin U.
This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.
The impact of surface roughness angle on cricket ball
(Springer, 2025-03) Jalan, Arun Kumar
For many years, perhaps even from the inception of the game itself, cricket players and spectators have been drawn to the aerodynamic properties of a cricket ball. We have compared the lift force and drag with observations. The Detached Eddy Simulation model (DES) has been used to provide a 2D numerical simulation of a cricket ball. DES was effectively used to simulate the cricket ball's lift and drag forces at various ball speeds, seam angles, and roughness heights. This study focuses on the computational fluid dynamic simulation of the cricket ball in the flow field. The flow field at different roughness angles 45° to 90° with a gradient of 15°, roughness heights in a range of 0.1 to 1 mm, and three bowling velocities of 32.77,35.55, and 45.55 m/s have been computationally analyzed. Aerodynamic forces were obtained using numerical analysis, while boundary layer separation sites were obtained through analysis. The aerodynamics of a cricket ball were proven by comparing the boundary layer separation width and asymmetry to the drag and lift forces.
Model based fault diagnosis in rotating machinery
(International Journal of Performability Engineering, 2011-11) Jalan, Arun Kumar
A continuing task in engineering is to increase the reliability, availability and safety of technical processes and to achieve these fault diagnosis becomes an advanced supervision tool in the present industries. Vibration in rotating machinery is mostly caused by unbalance, misalignment, shaft crack, mechanical looseness and other malfunctions. The objective of this paper is to propose a model based scheme for fault diagnosis of a rotor system. Presence of faults changes the dynamic behaviour of the system which is taken into account by equivalent loads acting on the healthy system model. In order to diagnose the faults in a rotor system the experimental time responses for healthy system as well as for faulty system were used. It was observed that the proposed scheme successfully detects and identifies the type, location and amount of fault in a rotor system for unbalance, misalignment and crack. This method has thus demonstrated the efficacy of the model based fault detection system for a simple rotor-bearing system.
Model based fault diagnosis of a rotor–bearing system for misalignment and unbalance under steady-state condition
(Elsevier, 2009-11) Jalan, Arun Kumar
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.
A Review on Fault Diagnosis of Misaligned Rotor Systems
(International Journal of Performability Engineering, 2020) Jalan, Arun Kumar
The diagnosis and prognosis of misaligned rotor systems have gained importance in recent times. Misalignment has become one of the main reasons for system vibration, which reduces the life and stability of machine parts, making it vitally important for machines to perform effectively without any catastrophic failure. Limited research has been reported on understanding its effect on rotor systems. Even if zero misalignment is achieved at the beginning, it cannot be retained over longer durations due to various reasons. Many techniques like DWT, CWT, and HHT are also used to understand the misalignment problem. Some advanced techniques such as MCSA, thermal imaging, and the acoustic emission technique have come into existence and become important tools to classify faults, leading to more reliable misalignment diagnosis. In the present study, a detailed literature review is conducted to diagnose and classify misalignments. All recent techniques and their limitations are discussed, and a hybrid approach is presented for the lucid understanding of this fault and its classification.
A review on mechanical behavior of natural fiber based hybrid composites
(Sage, 2012-06) Jalan, Arun Kumar; Shrivastava, Sharad
Hybrid composites are manufactured by combining two or more fibers in a single matrix. Hybrid composites can be made from artificial fibers, natural fibers and with a combination of both artificial and natural fibers. Hybrid composites can help us to achieve a better combination of properties than fiber reinforced composites. The constituent fibers in a hybrid composite can be altered in a number of ways leading to variation in its properties. The importance of this review can be attributed to the significant aspects of natural fiber based hybrid composites which are found to be predominantly affected by factors which include variation in fiber volume/weight fraction, variation in stacking sequence of fiber layers, fiber treatment and environmental conditions.
Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using Vibro-Acoustic Sensor Data Fusion
(Springer, 2022-01) Jalan, Arun Kumar; Marathe, Amol
In condition monitoring, accurate fault identification is an essential task for designing a proper maintenance strategy. Misalignment is one of the main faults in rotary machinery, because 70% of the failure occurs due to misalignment. Conventionally, the diagnosis of misalignment is carried out through vibration measurements. Especially, the presence of strong 2x vibration peak is generally accepted. Both angular and parallel misalignment shows peak at 2x, therefore, distinguishing misalignment type by using vibration signals alone is a difficult activity. This paper discusses classification of misalignment i.e., angular and parallel by using a diagnostic medium such as the acoustic emission and the rotor vibration signal. Vibro-acoustic sensors are used to collect data from the misaligned rotor system at two different loading, three different speed and three defect severity conditions. Time domain features are extracted and graded according to their significance using t test (One-way ANOVA) technique. Extracted features are used to train different algorithms. The outcome obtained using support vector machine (SVM) is 100% accurate. Vibro-acoustic sensor data fusion technique is employed to classify various forms of misalignment under different operating conditions. This work also intended to explore using a small amount of training data using different algorithms. The proposed method outperforms fault classification using vibration signal and acoustic signal separately.