Anurag Choudhary

• Doctor of Philosophy
• Post-doctoral Fellow at The University of Hong Kong

Industries are heavily dependent on rotating components, which are gradually deteriorate over time, resulting in system failure and financial losses. Numerous investigations have been conducted utilizing various methodologies to identify incipient faults in rotating components. But more focus is given to single component fault diagnosis. It is high...

The rotating machine components are interconnected. If the machines are not monitored properly, it causes damage to the connected parts, causing catastrophic failure. Dependability on a single sensor or sensors of the same modality for multi-fault diagnosis influences decision-making. Therefore, multi-modality multi-sensor fusion has been used to g...

With global efforts towards eco-friendly future transportation, electric vehicles (EVs’) are emerging as a potential solution to reduce carbon emissions and dependency on conventional fuels. In India, a busy metropolis battling air pollution and traffic congestion, the transition to EVs has great potential to address these challenges. The presented...

Electric vehicles (EVs) are essential for sustainable transportation, and various eco-friendly vehicles are being manufactured. In EVs, the traction motor is a crucial prime mover for propelling the vehicle forward. However, traction motors are susceptible to faults like any other motors which can compromise their performance, safety, and longevity...

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-fau...

Background: Multi-faults in rotating machines are critical and create an unfavourable working environment. Research on multi-faults is still in the early stages, and models are trained at constant speeds, which are not efficient under different types of uncertain speed conditions. Methodology: This paper proposes developing a multi-fault diagnosis...

A rotating machine consists of many components, and the bearing is one of the key components in it. The health condition of the bearing directly affects the safe operation of the rotating machine and also inhibits the catastrophic loss to other components. To receive high-quality goods and services from these machines, their proper functioning requ...

Electric Vehicles (EVs) are extremely efficient and produce zero emissions which is a better alternative than conventional IC engine vehicles. The electric motor is the heart of every EV and is the key to realizing the optimum balance of top speed, acceleration, deceleration, and achievable distance per charge. In such conditions, the continuous mo...

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 po...

The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelli...

Rotating machines frequently undergo various faults causing increased maintenance and operation costs. To minimize these costs, effective and intelligent methods are thus required. Different sensor modalities reflecting various faults should continuously be monitored and interpreted to enable these methods. In this work, two sensor modalities, Infr...

Electric motors are the core component in the Electric Vehicles (EVs) for providing rotational motion to the transmission system of the vehicle. Compared to diesel engines, these electric motors have no carbon emissions and are more reliable in operation. An early fault diagnostic system is necessary to ensure its effectiveness and reliability for...

Electric Vehicles (EVs) have grown in universality in recent years due to their specific advantages such as zero emissions, low noise, and remarkable efficiency. Moreover, these vehicles, especially two-wheelers becoming an integrated part of the present transport system, and their safe operation must be considered for a successful run under practi...

Induction motor (IM) is a highly efficient prime mover in industrial applications. To maintain an uninterrupted operation, accurate fault diagnosis system of IM is required. It can help to improve operational safety and prevent unexpected economic losses. The traditional diagnosis methods are less capable of dealing with real-time and varying worki...

Background: The widely used rolling element bearings in rotating machines undergo progressive degradation with continuous operation. To identify bearing faults, complex time-frequency based signal processing techniques and high-end deep neural network algorithms have been used to perform fault classification, which is time-consuming. Method: In...

Gears are crucial part of rotating machines and are used to transfer the motion as increased or decreased form. The faults in the gears could cause significant losses in these machines. So, there is a continuous need for an intelligent fault detection method that can detect these faults at the early stage. In this work, a sound signal-based early f...

The inevitable simultaneous formation of multiple faults in bearings generates severe vibration, propelling it for 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 multi-faults, which increases the data acquisition time an...

The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were...

Bearings are one of the crucial elements in rotating machinery and their malfunctioning is the major reason of machine failure. The identification and diagnosing of bearing performance deterioration is critical for the smooth and reliable operation of rotating equipment’s. This paper proposes an intelligent vibration-based condition monitoring and...

Diagnosis of multi-fault conditions of rotating machines is difficult from raw vibration signals. Time-frequency analysis can play an important role in this context. This article puts forward a comparative study of various time-frequency analysis like Short-time Fourier transform (STFT), Continuous Wavelet Transform (CWT) and Wigner Ville Distribut...

The perception of sound quality is an important source of information and can be used as a promising indicator to diagnose various faults in rotating machines. This work presented a methodology involving the detection of bearing faults using sound quality metrics. Head and Torso Simulator (HATS) was used to acquire the sound signals of different be...

Rotating elements are the essential part of various industries. Progressive degradation of rotating parts leads to system failure and economic losses. Several studies have been carried out to diagnose incipient faults in rotating components using the knowledge-based self-diagnosis Machine Learning (ML) models. But in real scenarios expecting the oc...

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...

Induction Motor (IM) is the most important prime mover in the automotive industry and has great potential in Electric Vehicles (EVs). IM failure is one of the most prevalent reasons for EV failure. This paper presents a fault diagnosis method for IM to enhance the efficiency, performance, and availability of EV and to reduce its maintenance costs....

Bearing is one of the core components of any rotating machine, and its failure is widespread. This reason drives continuous monitoring and detecting bearing faults during machine operation to warn operators and prevent unforeseen damage. This paper proposes an intelligent Passive Thermography (PTG) based fault diagnosis technique for detection of b...

Electric Vehicle (EV) is crucial for future transportation which will improve fuel economy and contributes toward reduction of emissions. EVs are becoming increasingly integrated component of transportation in order to fulfil ever-increasing demands for improved performance with safety and reduced environmental impact. Therefore, to increase the ef...

Faults in rolling element bearings are the main cause of rotating machine failure. Locating and isolating the faults has become a critical concern for the stable operation of rotating machinery. This paper puts forward a methodology to derive optimum fault indicators from vibration signature and to make a robust model using Support Vector Machine (...

Due to numerous shortcomings of the existing automobiles, the future of transportation lies in Electric Vehicles (EVs). For the sustainable existence of EVs, the development of charging infrastructure based on renewable sources is necessary. Hence, this research article aims to develop a PV–grid topology-based EV charger. An experimental prototype...

Fault diagnosis in rotating machines plays a vital role in various industries. Bearing is the essential element of rotating machines, and early fault detection can reduce the maintenance cost and enhance machine availability. In complex industrial machinery, a single sensor has a limitation to capture complete information about fault conditions. He...

Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for...

The Bearings are the crucial components of rotating machines in an industrial firm. Unplanned failure of these components not only increases the downtime, but also leads to production loss. This paper presents a non-invasive thermal image-based method for bearing fault diagnosis in rotating machines. Thermal images of rolling-element bearing in six...

Sensor technology is the backbone of diverse engineering and biomedical applications that has attracted the attention of academicians and researchers. Design and development of an efficient sensor requires a material that can monitor large structural changes in response to a small applied input with high degree of precision and consistency. Graphen...

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, lower maintenance costs and improve machine availability. Due to the increasing trend of Condition Monitoring (CM), numerous sensors deployed on industrial apparatus around the world and several monitoring techniques available for fau...

Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by...

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning met...

There is a constant call for reduction of operational and maintenance costs of induction motors (IMs). These costs can be significantly reduced if the health of the system is monitored regularly. This allows for early detection of the degeneration of the motor health, alleviating a proactive response, minimizing unscheduled downtime, and unexpected...