Related papers: Fault Detection in Induction Motors using Function…
Induction motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This paper presents a machine learning model for the fault detection and classification of induction…
A method for determining the current signature faults using Fractional Fourier Transform (FrFT) has been developed. The method has been applied to the real-time steady-state current of the inverter-fed high power induction motor for fault…
Condition monitoring of induction motor has been widely researched over recent years due to its ability to monitor operating characteristics and the health status of induction motor. Various methods have been used to monitor induction…
The detection and identification of induction motor faults using machine learning and signal processing is a valuable approach to avoiding plant disturbances and shutdowns in the context of Industry 4.0. In this work, we present a study on…
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility…
Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent.…
Early fault diagnosis is imperative for the proper functioning of rotating machines. It can reduce economic losses in the industry due to unexpected failures. Existing fault analysis methods are either expensive or demand expertise for the…
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing…
Fault diagnosis in marine diesel engines is vital for maritime safety and operational efficiency.These engines are integral to marine vessels, and their reliable performance is crucial for safenavigation. Swift identification and resolution…
Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional…
A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current…
This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to…
This paper describes two approaches for fault detection: an immune-based mechanism and a formal language algorithm. The first one is based on the feature of immune systems in distinguish any foreign cell from the body own cell. The formal…
The problem of estimating the parameters of induction motor models is considered, using the data measured by a circuit breaker equipped with industrial sensors. The measured data pertain to direct-on-line motor startups, during which the…
A majority of recent advancements related to the fault diagnosis of electrical motors are based on the assumption that training and testing data are drawn from the same distribution. However, the data distribution can vary across different…
Motor bearing fault detection (MBFD) is critical for maintaining the reliability and operational efficiency of industrial machinery. Early detection of bearing faults can prevent system failures, reduce operational downtime, and lower…
Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy…
The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless,…
De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements. Determining the Functional De-Rating of sequential logic cells typically requires computationally…
This paper presents a study on the reduction of the sampling frequency of the current signals of an induction motor, the reductions are performed by means time-decimation technique for digital signal processing. We have used the Fast…