On-line Diagnostics Research Articles

Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

Internet-based cognitive behavioral intervention for adolescents with anxiety disorders: a study protocol for a parallel three armed randomized controlled trial

BackgroundAnxiety disorders are among the most prevalent mental health concerns affecting children and adolescents. Despite their high prevalence, statistics indicate that fewer than 25% of individuals in this demographic seek professional assistance for their condition. Consequently, there is a pressing need to develop innovative interventions aimed at improving treatment accessibility. ObjectivesThis study aims to assess the effectiveness of Internet-delivered Cognitive Behavioral Therapy (iCBT) for adolescents with anxiety, with a specific emphasis on involving parents in the treatment process.MethodsThe study is structured as a parallel three-armed randomized controlled trial, comparing Internet-delivered Cognitive Behavioral Therapy (iCBT) with planned feedback, iCBT with on-demand feedback, and a waitlist control group, each group including 56 participants. Participants in the two iCBT conditions will undergo a 14-week treatment regimen, while those in the waitlist control group will wait for 14 weeks before starting iCBT with planned feedback. Additionally, participants in the iCBT groups will be randomly assigned to receive a booster session or not. The study design is factorial including two factors: type of therapist feedback (factor 1) and booster or no booster (factor 2). The study population comprises adolescents aged between 12 and 17 years, residing in Denmark, diagnosed with an anxiety disorder according to DSM-5 criteria. The primary outcome measures are the Youth Online Diagnostic Assessment and the Spence Children’s Anxiety Scale. Assessments will occur at baseline, post-treatment, and at 3-, 6-, and 12-month follow-ups post-treatment.DiscussionThe findings of this study are anticipated to contribute to improving the accessibility of evidence-based treatments for adolescents with anxiety.Trial registrationThe study is registered at clinicalTrials.gov, under protocol ID 22/59602. The Initial release was the 16.10.2023, first posted due to technical problems 16.04.2024. https://clinicaltrials.gov/study/NCT06368557?locStr=Odense,%20Denmark&country=Denmark&city=Odense&page=2&rank=13.

Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

ADHD in children and adolescents: Guideline-based online assessment in the consortium project INTEGRATE-ADHD.

The consortium project INTEGRATE-ADHD compared administrative data on the presence of attention-deficit/hyperactivity disorder (ADHD) in children and adolescents with the results of a parent survey and a comprehensive clinical assessment based on the S3 guideline of the Association of the Scientific Medical Societies in Germany (AWMF). Due to the COVID-19 pandemic, the clinical assessment was carried out online. The article describes how a guideline-based clinical assessment of ADHD can be implemented in an online setting. A specially developed diagnostic matrix is presented to illustrate the assessment procedures and the diagnostic decision-making process. The matrix is intended to help the diagnostician to gain an overview of the numerous individual findings that have been collected using different assessment perspectives and methods (e.g. diagnostic interviews, rating scales, performance tests) in order to make a well-founded and transparent diagnostic decision. The consortium project INTEGRATE-ADHD has shown that an online assessment can be implemented in a guideline-compliant manner and allows a valid clinical decision. The diagnostic strategy is discussed with reference to international guidelines and recommendations for online diagnostics (e.g. aspects of feasibility, acceptability and safety of the assessment procedures). The challenges and opportunities of using online assessments in clinical practice are also described.

In Vivo Regulation of Active Matrix Metalloproteinase-8 (aMMP-8) in Periodontitis: From Transcriptomics to Real-Time Online Diagnostics and Treatment Monitoring.

This study investigated in vivo regulation and levels of active matrix metalloproteinase-8 (aMMP-8), a major collagenolytic protease, in periodontitis. Twenty-seven adults with chronic periodontitis (CP) and 30 periodontally healthy controls (HC) were enrolled in immunohistochemistry and transcriptomics analytics in order to assess Treponema denticola (Td) dentilisin and MMP-8 immunoexpression, mRNA expression of MMP-8 and its regulators (IL-1β, MMP-2, MMP-7, TIMP-1). Furthermore, the periodontal anti-infective treatment effect was monitored by four different MMP-8 assays (aMMP-8-IFMA, aMMP-8-Oralyzer, MMP-8-activity [RFU/minute], and total MMP-8 by ELISA) among 12 CP (compared to 25 HC). Immunohistochemistry revealed significantly more Td-dentilisin and MMP-8 immunoreactivities in CP vs. HC. Transcriptomics revealed significantly elevated IL-1β and MMP-7 RNA expressions, and MMP-2 RNA was slightly reduced. No significant differences were recorded in the relatively low or barely detectable levels of MMP-8 mRNAs. Periodontal treatment significantly decreased all MMP-8 assay levels accompanied by the assessed clinical indices (periodontal probing depths, bleeding-on-probing, and visual plaque levels). However, active but not total MMP-8 levels persisted higher in CP than in periodontally healthy controls. In periodontal health, there are low aMMP-8 levels. The presence of Td-dentilisin in CP gingivae is associated with elevated aMMP-8 levels, potentially contributing to a higher risk of active periodontal tissue collagenolysis and progression of periodontitis. This can be detected by aMMP-8-specific assays and online/real-time aMMP-8 chair-side testing.

Development of a fault diagnostics and tolerance system: An application to continuous stirred tank reactor

Fault diagnosis and tolerance are crucial for monitoring system health and ensuring stability in industrial processes. Challenges arise in designing fault diagnostic solutions for real-time industrial processes with inherent nonlinear dynamic behaviors, particularly when dealing with multiple operating regions characterized by varying dynamics. This article addresses this challenge and proposes a fault diagnostic and tolerant control scheme for industrial systems. The proposed approach integrates a fuzzy-based realization technique with a subspace-aided methodology to effectively handle the nonlinear dynamic behavior observed across different operational scenarios. A practical solution is presented, significantly reducing the computational burden associated with online diagnostics, as the parity vectors are computed offline using available input–output data for different operating regions. During online diagnostics, only computed parity spaces are used with fuzzy realizations for residual generation, leading to a significant reduction in online computation. Numerical examples demonstrate the effectiveness of the proposed method, achieving a high precision rate in fault diagnostics. Furthermore, the diagnostic methodology is integrated with fault-tolerant control for practical applications, as demonstrated in the application of a continuous stirred tank reactor. This integration enables the system to effectively tolerate faults and ensure sub-optimal operation of the industrial process.

Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions

In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators.

Quality of life after COVID-19 in Polish patients

The pandemic is a new, surprising situation that shows the quality of life in a completely different dimension. Studies show that the quality of life of people infected with SARSCov2 has signifi cantly decreased both psychologically and physically. Covid 19 disease caused by coronavirus SARS-CoV-2 has been shown to be a complex pathogenic disease with high mortality rates, especially in the elderly, but many serious cases and deaths occurred in young people. The aim of the study was to investigate whether COVID 19 disease signifi cantly affects the quality of life of those affected. The method used for online diagnostics was the EQ-5D-3L Quality of Life Questionnaire and a modifi ed BAT questionnaire. Studies have shown that the quality of life of the respondents was at an average level. Pain/discomfort and anxiety/depression were the most frequently indicated domains. A statistically signifi cant strong relationship between the demographic variables age, gender and morbidity and quality of life after the disease was shown. Similarly, the effects of hospital stays and concomitant illnesses affecting quality of life were investigated. The results can feed into medical practice, therapy, education and therapy of mental health, and the applied research model can be considered useful and useful to continue research. Keywords: COVID-19, quality of life, patients.

A Total Self Checking Comparator Implementable on FPGAS Using Bist Technology

an integrated circuits (IC) "manufacturing tests" may be made easier to administer with the use of design for testability (DFT). Integrated circuits' embedded memory tests make use of the TSC (TSC) approach. We have shown the TSC method and several algorithms used in TSC for the purpose of testing embedded memory in this article. An address generator, controller, comparator, and memory are the four main components of this kind of memory TSC technology. This paper details the three memory TSC controller implementation techniques. The memory TSC controller is modelled in Verilog HDL, and its accuracy is checked using the RTL compiler before synthesis.
 Here we provide a way to build TSC comparators for TSC systems that may be implemented on FPGAs—totally self-checking (TSC) systems—that can be used online. By directly measuring the output of each lookup table (LUT), this approach may be utilised to do comprehensive online diagnostics of all LUTs. This entails mapping the basic components of the comparator with a limited number of test patterns. With our technique, we can achieve exhaustive diagnosis with a small number of test patterns on the order of n [O(n)] (where n is the input number to the comparator) while yet covering all bases 100% of the time, even if we are just aware of the LUT's specs and not its exact structure. For systems that need absolute reliability, FPGAs will be a perfect fit. Our experiment also included a single-event upset (SEU) induced by neutron radiation to validate the soft error rate (SER) in a field-programmable gate array (FPGA) based on static random-access memory (SRAM).

A voltage reconstruction model for lithium-ion batteries considering the polarization process

Health assessment is one of the most crucial components in lithium-ion battery (LIB) management. However, traditional methods often require disassembling LIBs, which are inconvenient for implementation. On the contrary, non-destructive diagnostics can obtain the aging information of LIBs without causing damage to batteries, which is beneficial for the periodic inspection and secondary use of LIBs in other fields. Therefore, accurate non-destructive diagnostics is necessary in practical applications. This paper proposes a new voltage reconstruction model considering the polarization process to get the battery internal information. Through matching the equilibrium potentials of the positive and negative electrodes, combined with an equivalent circuit model, this voltage reconstruction model can accurately simulate the whole charging and discharging processes of LIBs. The parameters with clear physical interpretation in the model can be quickly and accurately identified within the MATLAB software, and these parameters can be used to quantitatively identify the degradation modes of LIBs. Finally, the post-mortem analysis of the aged LIB is used for verification. The results show that the proposed model could precisely reflect the battery internal ageing information and could be used for online diagnostics in real electric vehicles.

Wireless Hybrid Vehicle Three-Phase Motor Diagnosis Using Z-Freq Due to Unbalance Fault

Online diagnostics of three phase motor rotor faults of hybrid vehicle can be identified using a method called machine learning. Unfortunately, there is still a constraint in achieving a high success rate because a huge volume of training data is required. These faults were represented on its frequency content throughout the Fast Fourier Transform (FFT) algorithm to observe data acquired from multi-signal sensors. At that point, these failure-induced faults studies were improved using an enhanced statistical frequency-based analysis named Z-freq to optimize the study. This analysis is an investigation of the frequency domain of data acquired from the turbine blade after it runs under a specific condition. During the experiment, the faults were simulated by equipment with all those four conditions including normal mode. The failure induced by fault signals from static, coupled and dynamic were measured using high sensitivity, space-saving and a durable piezo-based sensor called a wireless accelerometer. The obtained result and analysis showed a significant pattern in the coefficient value and distribution of Z-freq data scattered for all flaws. Finally, the simulation and experimental output were verified and validated in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. This outcome has a great prospect to diagnose and monitor hybrid electric motor wirelessly.

Preference-Based Multi-Robot Planning for Nuclear Power Plant Online Monitoring and Diagnostics

Current preventative maintenance paradigms in nuclear power plants carry several costly risks and challenges associated with component downtime and the need for human data collection. Preventative maintenance may be enabled by an online monitoring system that accurately assesses component condition and identifies potential faults. We present an approach for autonomous online monitoring and multiagent planning for robotic data collection. Under the occurrence of a fault, we utilize a machine learning model to form an initial guess of its nature, which we then refine by selectively measuring certain variables to gain additional information via a situation-aware variable selection model. To generate a multi-robot plan to conduct these measurements, we develop a preference-based planning framework within a linear temporal logic–based planning approach that prioritizes collecting data from the most important features. Finally, we demonstrate our approach on a case study using a simulated nuclear power plant circulating water system, showing fault diagnostic performance as well as simulated robot data collection.

Online Diagnostic Lab Reporting System

The project “ONLINE DIAGNOSTIC LAB REPORTING SYSTEM” is to develop web application to computerize the Management of a Diagnostic System which is user friendly simple, fast, and cost-effective. It deals with the collection of patient’s information, diagnosis details, etc. Traditionally, it was done manually. The main function of the system is register and store patient details and staff details and retrieve these details as and when required, and also to manipulate these details meaningfully. System input contains patient details, diagnosis details, while system output is to get these details on to the screen. The Online Diagnostic Lab Reporting System can be entered using a username and password. It is accessible by every level of user according to their role. Every user can view the data that they are accessible. Only that user can add data into the database who has editing permission. The data can be retrieved easily. The data are well protected for personal use and makes the data processing very fast.

Using long-term condition monitoring data with non-Gaussian noise for online diagnostics

The number of timely diagnoses based on condition monitoring data is increasing with the growing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method’s efficacy.

Identification of Selected Failures in a Pipe Conveyor’s Operation with the Use of the Discrimination Method Based on Continuous Measurement

This paper deals with research on the operational process monitoring of a pipe conveyor for the needs of online diagnostics. The aim of this research is to verify the possibility of identifying the selected pipe conveyor’s failures in its straight section during operation (a missing roller in the idler housing, absent material on the conveyor belt) with the use of a discrimination method. This is an attempt to implement digital transformation with the aim of verifying its possibilities and limitations. The basis for discrimination is a continuous measurement and evaluation of measured values of contact forces in certain rollers’ positions in the hexagonal idler housing. Within this research, eight different measurement regimes were implemented. The use of the method was verified with simulated data using the trace table. We aimed to create prerequisites for online monitoring, which, based on digital transformation, will be deployed to control a transport system. The measurement was realized with the maximum tension force of 28,000 N. From the measurements, a decision-making algorithm was proposed to identify selected failures in the pipe conveyor operation with the use of the discrimination method. Within the algorithm, classifying criteria were determined, in the range of 57 N ÷ 251 N. The results confirm the method’s suitability for its practical assurance of pipe conveyors’ failure-free operation, as the failures were always identified sufficiently in advance, thanks to which, in practice, there was no further damage to the diagnosed devices.

A Novel Indicator-Based Online Diagnostics Technique of Interturn Short-Circuit Faults in Synchronous Reluctance Machines

In recent years, the adoption of Synchronous Reluctance Machine (SynRM) drives for safety-critical equipment has witnessed notorious growth. The difficulties inherent to monitoring and fault diagnostics of SynRM drives are highly prominent. Inter-turn short-circuit faults (ITSCFs) are among the failure modes which are most critical and, simultaneously, more challenging to identify, in all types of motor drives. Still, the effective diagnostic task becomes even more challenging in SynRM drives, because of the harmonic pollution and intricated control configuration. To overcome the aforementioned challenges, the current research proposes a universal and effective online diagnostics technique, based on the computation and monitoring of a relevant severity factor, defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter’s estimation and reduced requirements regarding variables’ measurements make the proposed approach extremely advantageous for motor drives with complex control architecture and operating conditions. The experimental results demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness. To confirm the claims, the proposed technique is implemented on a 2.2 kW SynRM drive, tested in both healthy and faulty conditions.

Online Diagnosis and Discrimination of Stator Faults in Six-Phase Induction Motors Based on Voltage Symmetrical Components

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines, mainly due to their ability to operate smoothly under fault conditions. Due to multiple stator phase arrangements, existing fault diagnostic (FD) methods are not sufficiently universal and cannot be applied to the multiple configurations of multiphase machines. In this regard, the current research proposes a universal and effective online diagnostics technique based on the computation and monitoring of a relevant severity factor, which is defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter estimation and limited requirements regarding variables measurements make the proposed approach extremely advantageous for motors with high number of phases. This article addresses at stator faults in symmetrical six-phase induction machines (6PIM). The proposed method’s experimental results on six-phase motor drive platforms demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness, along with the ability to accurately distinguish between the occurrence of Inter-Turn Short Circuit (ITSC) faults and the presence of Unbalance Supply Voltage (USV) conditions. A 6PIM is put through its paces in both healthy and faulty conditions. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults and USV operating conditions with high reliability, fastness and high precision.

Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator.

This paper considers the active fault isolation problem for a class of uncertain multimode fault systems with a high-dimensional state-space model. It has been observed that the existing approaches in the literature based on a steady-state active fault isolation method are often accompanied by a large delay in making the correct isolation decision. To reduce such fault isolation latency significantly, this paper proposes a fast online active fault isolation method based on the construction of residual transient-state reachable set and transient-state separating hyperplane. The novelty and benefit of this strategy lies in the embedding of a new component called the set separation indicator, which is designed offline to distinguish the residual transient-state reachable sets of different system configurations at any given moment. Based on the results delivered by the set separation indicator, one can determine the specific moments at which the deterministic isolation is to be implemented during online diagnostics. Meanwhile, some alternative constant inputs can also be evaluated for isolation effects to determine better auxiliary excitation signals with smaller amplitudes and more differentiated separating hyperplanes. The validity of these results is verified by both a numerical comparison and an FPGA-in-loop experiment.

MARGE — a new ModulAr Robotic Gas-jet targEt system for chemistry studies with homologues of superheavy elements

A new ModulAr Robotic Gas-jet targEt (MARGE) system for chemistry studies with homologues of SuperHeavy Elements (SHEs) was designed, constructed, and successfully tested on a beam line at the U-120M accelerator in NPI in Řež, near Prague. The installation of the MARGE system represents an important milestone in research in the field of homologues of SHEs in the Czech Republic. The MARGE system utilizes a robotic target switching mechanism that is remotely controlled from the laboratory. A significant advantage of this functionality is that targets can be switched without personnel having to enter the cyclotron vault, which greatly reduces the radiation exposure of the personnel, as well as prevents long delays due to the necessary cool-down time. The MARGE system is completely modular, allowing for easy upgrades or maintenance. One of the modules houses a new four-pole beam position monitor, which allows for more precise ion beam focusing and on-line diagnostics, when compared with the previous circular one-pole design. The heart of the MARGE system, the Recoil-Transfer Chamber (RTC) module, is designed as a standalone building block with the possibility of stacking multiple RTC modules. In this way, the residual beam exiting the first module can be used to produce radionuclides in a second RTC module, thus enabling the simultaneous collection and transport of atoms recoiling from more than one target foil. The system was tested during two experimental campaigns in spring and autumn of 2021. This included an optimization of the transport yield of prepared radionuclides, and a measurement of the cross sections of the following reactions: 197Au (3He, 6n) 194mTl, 165Ho (3He, 5n) 163Tm, natPd (3He, xn) 104Cd, and natPd (3He, yn) 105Cd. The transport yield reached during the pilot test was 38 ± 2%. The performance of the system, comprising two stacked RTC modules, was verified during the third campaign in autumn of 2022.

Online Interturn Short-Circuit Fault Diagnosis in Induction Motors Operating Under Unbalanced Supply Voltage and Load Variations, Using the STLSP Technique

It is well known that the most common reason for electrical machines breakdown is the stator windings’ fault occurrence. Indeed, this type of fault represents almost 40% of faults occurring in induction machines. One of the major causes of stator defects is the occurrence of interturn short-circuit (ITSC) faults that have critical and dangerous effects on the motor itself, as well as on the related electrical equipment. Therefore, early detection and a precise severity estimation of the occurrence of ITSC faults for all working conditions can prevent failure breakdowns and increase reliability and safety of industrial facilities. In this context, this article proposes an efficient online diagnostics method based on calculating and monitoring a pertinent severity factor defined as the ratio between the zero and positive voltage symmetrical components. The online implementation of this method is performed on a LabVIEW environment, using the short-time least square Prony's (STLSP) method. It does not need any estimation of motor parameters and it requires only voltage sensors. Several tests under healthy and faulty conditions are carried out on a three-phase 3-kW induction motor. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults with high reliability, fastness, and high precision, even under load variations or unbalanced supply voltage.