Identification and Experimental Validation of an Induction Motor Thermal Model for Improved Drivetrain Design

The ability of an electric powertrain to perform according to mechanical specifications is equally important as assessing its thermal protection limits, which are affected by its electrical and thermal properties. Although rated parameters (such as power, torque, etc.) are easily accessible in catalogs of equipment producers, more specific properties like mass/length of copper winding, heat dissipation factor, etc., are not available to customers. Therefore, an effective selection of drivetrain components is limited due to the lack of sufficient data and the need to consult critical design decisions with suppliers. To overcome this limitation, we propose a method to estimate the temperature…

Investigation and reduction of losses on inverter-fed induction motors

An electric motor is more effective and flexible when supplied by a frequency converter. The frequency converter not only produces the fundamental voltage but also a set of higher harmonics which cause additional losses in the motor. Losses in the frequency converter are normally neglected in the drive dimensioning due to insufficient data available from manufacturers. Motor's losses can be reduced by increasing the switching frequency of frequency converters. An increase of the switching frequency may result in higher losses in the frequency converter. This work investigates analytically and experimentally the dependence of the losses of modern motor and frequency converter on a switching …

Hybrid Three-Phase Transformer-Based Multilevel Inverter With Reduced Component Count

The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmet…

Voltage Source Multilevel Inverters With Reduced Device Count: Topological Review and Novel Comparative Factors

Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems due to their features of high-quality produced waveforms, modularity, transformerless operation, voltage, and current scalability, and fault-tolerant operation. However, these merits usually come with the cost of a high number of components. Over the past few years, proposing new MLIs with a lower component count has been one of the most active topics in power electronics. The first aim of this article is to update and summarize the recently developed multilevel topologies with a reduced component count, based on their advantages, disadvantages, construction, and specific applications. Within …

Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life

Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.

Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines

Prediction of machines' faulty parts is important in industrial applications in order to reduce productivity losses. As far as electrical machines are considered, a model-based fault diagnosis approach is usually used for this purpose. The model is derived from the modified winding function theory and hence, it requires a considerable amount of parameters at various operating conditions in order to be successfully used. However, the complete set of parameters is difficult to be obtained, as manufacturers of electric machines normally provide only the parameters that describe simple motor models (e.g. T-equivalent circuit at rated conditions). Therefore, the current work presents a method th…

Novel Isolated Multiple-Input Buck-Boost DC-DC Converter for Renewable Energy Sources

An isolated multiple input dc-dc converter (MIC) with unidirectional buck-boost characteristics and simultaneous power transfer is proposed for multi-sources in renewable energy systems in this paper. When compared to existing isolated MICs, the proposed MIC significantly reduces the component count and control complexity since it requires a fixed coupled inductor with only one primary and secondary winding each for any number of inputs and does not require any phase-shifted pulse-width modulation. The operation of the proposed converter for simultaneous power transfer from multiple sources with varying voltages is numerically verified in simulation and validated on OPAL-RT’s OP5700 hardwar…

Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is propos…

Novel Three-Phase Multi-Level Inverter with Reduced Components

A new multilevel converter topology is proposed in this paper. Low component count and compact design are the main features of the proposed topology. Furthermore, the proposed converter is a capacitor-, inductor-, and diode-free configuration, allowing reducing the converter footprint, increasing the lifetime and simplifying the control strategy. Further, a comparative study is carried out to highlight the merits of the proposed circuit as compared to existing multilevel topologies. Finally, simulation results for the three-level version using different modulation strategies are presented.

New Multilevel Inverter Topology with Reduced Component Count

This paper introduces a new topology of modular multilevel inverters, being suitable in medium and high voltage applications. As compared to the existing circuits, the proposed topology has advantages of high ‘levels/components’ ratio, increasing the output voltage levels without increasing the voltage stress across the used switches, structure simplicity, isolation features, and modularity. These merits allow it to fit well in high-reliability medium-power applications, which require fast troubleshooting and maintenance flexibility. Operating principles of the proposed scheme are detailed in low frequency and pulse width modulation. Simulation and experimental results validate the effectiv…

Improved Quadratic Time-frequency Distributions for Detecting Inter-turn Short Circuits of PMSMs in Transient States

This paper aims to improve quadratic time-frequency distributions to adapt condition monitoring of electrical machines in transient states. Short-Time Fourier transform (STFT) has been a baseline signal processing technique for detecting fault characteristic frequencies. However, limits of window sizes due to loss of frequency- or time-resolution, make it hard to capture rapid changes in frequencies. Within this study, Choi-Williams and Wigner-Ville distributions are proposed to effectively detect peaks at characteristic frequencies while still maintaining low computation time. The improved quadratic time-frequency distributions allow for generating spectrograms of a longer lasting data sig…

Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors

The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at differ…

Early detection and classification of bearing faults using support vector machine algorithm

Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…

Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm

Online bearing fault detection is an important method for monitoring the health status of bearings in critical machines. This work proposes a classification algorithm, which can be extended towards an online bearing fault detection. The objective is to detect and classify the bearing faults in early stages. The overall design aspects of the online bearing fault detection and classification system are discussed. The proposed method is validated using experimental data, and a high accuracy of the fault classification was observed. Therefore, the proposed method can be applied for an online early fault detection and classification system.

Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning

Online condition monitoring and fault diagnosis systems are necessary to prevent unexpected downtimes in critical electric powertrains. The machine learning algorithms provide a better way to diagnose faults in complex cases, such as mixed faults and/or in variable speed conditions. Most of studies focus on training phases of the machine learning algorithms, but the development of the trained machine learning algorithms for an online diagnosis system is not detailed. In this study, a complete procedure of training and implementation of an online fault diagnosis system is presented and discussed. Aspects of the development of an online fault diagnosis based on machine learning algorithms are…

Bearing fault diagnosis for inverter-fed motors via resonant filters

Current-based technique is an economic solution to detect bearing faults in drive-trains. Localized faults produce characteristic vibration frequencies. When an electric motor is supplied by a frequency-converter, the current response includes not only the fundamental and fault related frequencies but also higher harmonics from the inverter. This paper introduces a resonant filter to pick up frequency components caused by the localized faults. The bearing fault frequencies are calculated by bearing geometry and motor speeds. The filter frequencies are selected as a function of motor speeds. The filter is independent of the load condition, so it can work at different motor operating points t…

Four Quadrant Switch Based Multiple-Input DC-DC Converter

In this paper, a novel non-isolated multiple input dc-dc converter (MIC) is proposed. The MIC uses four-quadrant switches, only one inductor and capacitor. It is capable of bidirectional operation in non-inverting buck-boost configuration and can accommodate the simultaneous transfer of energy from more than one source of different voltage levels to the DC bus. This MIC is analysed for two inputs in this paper. As compared to existing MICs in literature, the proposed converter utilizes less number of inductors and requires only one switch to integrate any extra energy storage. Different operation modes of the proposed MIC are numerically verified and validated on a high-fidelity hardware-in…

Control of ultra-high switching frequency power converters using virtual flux-based direct power control

This paper presents a control technique for ultra-high switching frequency rectifiers known as virtual flux-based direct power control (VF-DPC). This method utilizes AC voltage sensors to provide better power quality at the input and output of power converters. Performance of an ultra-high switching frequency converter using the extended and conventional VF-DPC method is analyzed. The analysis has shown that the extended VF-DPC method can reduce better the total harmonic distortion (THD) of input currents and the ripples of the output DC voltage as compared to conventional VF-DPC. Finally, the effect of switching frequency and inductance size on rectifier operation is investigated in this s…

Detecting Eccentricity and Demagnetization Fault of Permanent Magnet Synchronous Generators in Transient State

Eccentricity and demagnetization fault of a four-pole 1.5 kW surface mounted permanent-magnet synchronous-generator (PMSG) were modelled by using time-discretised finite element analysis (FEA). Both fault types are caused by magnetic asymmetry in the generator. The faulty behaviour of a PMSG under transient operating condition is studied with FEA. Two search coils were wound around stator teeth on opposite sides of the rotor. The induced voltage from these coils will be equal in healthy case. A fault is detected when the induced voltages are non-identical. The simulation results revealed that the envelope of the induced search coil voltage had sinusoids during dynamic eccentricity and demag…

Parameter sensitivity of flux-linkage based sensorless control for permanent magnet synchronous motors

Sensorless control can be utilized to reduce cost, size and total complexity of a motor drive or enhance reliability of the system. This paper first presents a sensorless control algorithm for a surface permanent-magnet synchronous motor (SPMSM) based on estimated flux linkages and stator currents. Within the algorithm, rotor position error can be predicted by comparing the estimated currents with measured stator currents. Performance of the sensorless control based on flux-linkages and the dependency of the algorithm on motor parameters is then numerically investigated via simulations. It is found from the investigation that the accuracy of the method depends on the motor working condition…

Identification of parameters and harmonic losses of a deep-bar induction motor

High frequency harmonics from a frequency converter causes additional losses in a deep-bar induction motor. The harmonics have their own amplitude and phase with respect to the fundamental signal, but the harmonic loss is only dependent on the amplitude of harmonics. A deep-bar induction motor can be modelled by a triple-cage circuit to take skin effect into account. The triple cage circuit having many parameters could be estimated from a small-signal model of the machine by using Differential Evolution. The correctly estimated parameters make the triple-cage circuit valid in a wide range of frequencies. However, the triple-cage circuit is very complicated which makes it difficult to model …

CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features

Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…

Identification of induction motor thermal model for improved drivetrain design

Selection of components of electric drivetrains is not only based on evaluating their ability to perform according to mechanical specifications, but — what is equally important — on assessing their thermal protection limits. These are typically affected by electrical and thermal properties of motors and drives. Although rated parameters (such as power, torque, speed, etc.) are easily accessible in catalogs of equipment producers, more specific properties like mass / length of copper winding, heat dissipation factor, rotor / stator dimensions etc. are not available to customers. Therefore, effective selection of drivetrain components is limited due to the lack of sufficient data and the need…

Bearing fault detection based on time-frequency representations of vibration signals

To prevent failures of a rolling bearing in the gearbox drive system, acceleration sensors are used to detect fault-related signals of the bearing. It is a big challenge to observe and identify signals caused by bearing defects in the time domain or the frequency spectrum by a conventional Fourier analysis. The time-frequency representation of the fault-related signals implemented by the windowed Fourier transform is studied in this work. It is shown that the fault characteristic frequencies can be clearly identified in the time-frequency spectrum if a fault occurs in the bearing of the gearbox at different speeds. Otherwise, the shaft frequency and its multiples are the main harmonics in t…

Bearing fault detection for drivetrains using adaptive filters based wavelet transform

Predicting a localized defect on a rolling bearing during the degradation process before a complete failure is crucial to prevent system failures, unscheduled downtimes and substantial loss of productivity. During this process, impulses associated with the fault are weak, nonstationary or time-frequency varying, and contaminated by noises, which render the problem of extracting these impulses very difficult. This work investigates the effectiveness of common signal processing techniques on predicting incipient faults, e.g. Fast Fourier transform, Short-Time Fourier transform, Wavelet transform. It was found that an adaptive filter is required to enhance and reconstruct the signals during th…

Four-Level Three-Phase Inverter With Reduced Component Count for Low and Medium Voltage Applications

This paper proposes a novel three-phase topology with a reduced component count for low- and medium-voltage systems. It requires three bidirectional switches and twelve unidirectional switches for producing four-level voltages without using flying capacitors or clamping diodes, reducing the size, cost, and losses. Removing flying capacitors and clamping diodes allows it to simplify control algorithms and increase the reliability, efficiency, and lifetime. A modified low-frequency modulation (LFM) scheme is developed and implemented on the proposed topology to produce a staircase voltage with four steps. Further, a level-shifted pulse width modulation (LSPWM) is used to reduce the filter siz…

Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning

Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.

Experimental Investigation of Efficiency Map for an Inverter-Fed Surface-Mount Permanent Magnet Synchronous Motor

Losses in inverter-fed permanent magnet motors are underestimated by using analytical or numerical approach since additional losses due to extra harmonics of the frequency converter are normally skipped. Further, losses in switches and passive components of the converter and the effect of switching frequencies cannot be numerically taken into consideration. Loss-minimizing control and proper efficiency analysis of inverter-fed permanent magnet motors cannot be achieved if an efficiency map is built based on a numerical investigation of the motors alone. This works first reviews losses in a surface-mount permanent magnet synchronous motor (SPMSM) and frequency converters. The efficiency map …

Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults

Detection and isolation of single and mixed faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of multilayer perceptron (MLP)- and convolutional neural network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional metho…

Temperature Rise Estimation of Induction Motor Drives Based on Loadability Curves to Facilitate Design of Electric Powertrains

Thermal protection limits are equally important as mechanical specifications when designing electric drivetrains. However, properties of motor drives like mass/length of copper winding or heat dissipation factor are not available in producers’ catalogs. The lack of this essential data prevents the effective selection of drivetrain components and makes it necessary to consult critical design decisions with equipment's suppliers. Therefore, in this paper, the popular loadability curves that are available in catalogs become a basis to formulate a method that allows to estimate temperature rise of motor drives. The current technique allows for evaluating a temperature rise of a motor drive for …

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor t…

Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network

Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…

Electromagnetic and Thermal Modelling for Calculating Ageing Rate of Distribution Transformers

Prediction of the lifetime for transformers is very important for maintenance and asset management. Finite element analysis was performed on a 5 MVA distribution transformers with aluminium foil-type windings and voltage rating 6600 V/23000 V. Electromagnetic modelling is implemented on the full three-phase transformer to calculate distributed losses, taking the skin effect into account. To reduce the computational burden, the distributed losses in one phase are used to analyse temperature rise in one phase of the transformer. The temperature rise results were used to determine the ageing rate of the transformer. Further, the influence of ambient temperature and cooling on the temperature r…

Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains

This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…

Modelling Demagnetized Permanent Magnet Synchronous Generators using Permeance Network Model with Variable Flux Sources

The partial demagnetization in a four-pole 1.5 kW surface mounted permanent-magnet synchronous-generator was modeled by permeance network model (PNM). The results were compared to a 2-D time-stepping finite element analysis (FEA). Both models where simulated in scenarios where one of the magnets where 20 % and 100 % demagntized and when none of the magnets where demagnetised. The results showed that the proposed PNM with variable magnetic flux sources matched the results of the FEA. The proposed method only need to invers the permeance matrix once before the time simulation, while the traditinal PNM need to invers it in every time step. This make the proposed model less computationally heav…

Novel Threshold Calculations for Remaining Useful Lifetime Estimation of Rolling Element Bearings

The prognostics objective is to avoid sudden machinery breakdowns and to estimate the remaining useful life after initial degradation. Typically, physical health indicators are derived from available sensor data, and a mathematical model is tuned to fit them. The time it takes for the model to reach a failure threshold is the estimated remaining useful life. The failure threshold may be determined from historical failure data, but that is not always readily available. ISO standard 10816–3 defines permissible velocity vibration levels for machines that may be used as a failure threshold. However, velocity vibration is not suitable for bearing prognostics due to the effect of integration from…

Multi-band identification for enhancing bearing fault detection in variable speed conditions

Abstract Rolling element bearings are crucial components in rotating machinery, and avoiding unexpected breakdowns using fault detection methods is an increased demand in industry today. Variable speed conditions render a challenge for vibration-based fault diagnosis due to the non-stationary impact frequency. Computed order tracking transforms the vibration signal from time domain to the shaft-angle domain, allowing order analysis with the envelope spectrum. To enhance fault detection, the bearing resonance frequency region is isolated in the raw signal prior to order tracking. Identification of this region is not trivial but may be estimated using kurtosis-based methods reported in the li…

Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the sugg…

Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets

Authors accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper proposes an active learning scheme to detect multiple faults in permanent magnet synchronous motors in dynamic operations without using historical labelled faulty training data. The proposed method combines the self-supervised anomaly detector based on a local outlier factor…

Field Reconstruction for Modeling Multiple Faults in Permanent Magnet Synchronous Motors in Transient States

Conventional field reconstruction model (FRM) for electrical machines has proved its main strength in efficient computations of magnetic fields and forces in healthy permanent magnet synchronous machines (PMSM) or faulty machines in steady states. This study aims to develop a magnet library of different magnet defects and include inter-turn short-circuit (ITSC) in the FRM for PMSM. The developed FRM can model a combination fault between ITSC, and magnet defect in a PMSM in transient states. Within the framework, an 8-turn ITSC was modelled in both finite element analysis (FEA) and FRM, and then identified by the extended Park’s vector approach. The air-gap magnetic field reproduced b…

Form-wound stator winding for high-speed induction motors

Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework

This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 …

Diagnosis of Incipient Bearing Faults using Convolutional Neural Networks

The majority of faults occurring in rotating electrical machinery is attributed to bearings. To reduce downtime, it is desired to apply various diagnostic methods so that bearing degradation can be detected in good time prior to a complete failure. The work presented in this paper utilizes a data-driven machine learning approach based on convolutional neural networks (CNNs) in order to diagnose different types of bearing faults. A one-dimensional CNN is trained on vibration signals and compared to a two-dimensional CNN trained in time-frequency domain using continuous wavelet transform (CWT). The proposed method is demonstrated on data collected from run-to-failure tests.The results show th…

Novel Three-Phase Multilevel Inverter With Reduced Components for Low- and High-Voltage Applications

In this article, a novel multilevel topology for three-phase applications, having three-level and hybrid N -level modular configurations, enabling low-, medium-, and high-voltage operations, is presented. The proposed topology has several attractive features, namely reduced component count, being capacitor-, inductor-, and diode-free, lowering cost, control-complexity, and size, and can operate in a wide range of voltages and powers. Selected simulation and experimental results are presented to verify the performance of the proposed topology. Further, the overall efficiency of the topology and loss distribution in switches are studied. Finally, the key features of the proposed topology in t…