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This article studies the influence of fiber Bragg grating (FBG) head length on insulated gate bipolar transistor (IGBT) direct on-chip thermal sensing performance of FBG sensors. To this end, the surface of a commercial IGBT chip is thermally simulated and experimentally characterized. Uniform FBG sensors with three head sizes are then tested in two promising thermal sensing locations. The study has found that the large thermal gradients in this application create an additional constraint when using longer head lengths. A distortion in the reflected spectrum of the 5-mm FBG sensor is used to illustrate the underlying physical effect, which causes this limitation for IGBT junction temperature measurement. This additionally affects the length of head sizes providing accurate temperature readings of the IGBT surface hotspots, and significantly, this limit is location-dependent in a given IGBT geometry.

this paper reports a distributed thermal monitoring scheme for power electronic modules (PEMs) in wind turbine converters. The sensing system is based on utilizing electrically non-conductive and electromagnetic interference immune fiber Bragg Grating (FBG) sensing technology embedded in the PEM baseplate. The design and implementation features of the proposed scheme are presented first. The scheme is then applied in a commercial PEM operating within an inverter bridge, equipped also with a conventional distributed thermal monitoring system using a multiple point thermo-couple (TC) sensor suite. A range of tests are performed to evaluate the performance of the FBG distributed thermal monitoring system and correlate it to TC measurements under steady-state and transient operating conditions representative of PEM operation in an actual wind turbine application. It is shown that the proposed FBG monitoring system can offer practical operational improvements in establishment of distributed thermal sensing schemes for wind turbine PEM.

An IGBT finite element model aimed at facilitating the assessment of Fibre Bragg Grating sensing based direct on-chip temperature monitoring is first presented in this paper. A commercial off-the-shelf IGBT module geometry is characterised, and a thermo-electrical finite element model is then established using its features. The model is subsequently employed to evaluate the local thermal disturbances that may be introduced by the installation of the optical based temperature sensor directly on the top surface of the IGBT chip. The effects of the sensor thermal interface material characteristics, e.g., thermal conductivity and size, in the local temperature environment are investigated.

To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

Detection of winding faults in permanent magnet synchronous machines (PMSMs) with stranded winding designs remains a challenging task for conventional diagnostic techniques. This paper proposes a new sensing approach to this problem by investigating the application of dedicated electrically non-conductive and electromagnetic interference immune fiber Bragg grating (FBG) temperature sensors embedded in PMSM windings to enable winding open-circuit fault diagnosis based on observing the fault thermal signature. The final element analysis thermal and electromagnetic models of the examined practical PMSM design are first developed and used to enable the understanding of open-circuit winding fault-induced signature that can be used for effective diagnostic purposes, indicating in situ thermal excitation as an optimal diagnostic measurand. A purpose build test rig with an inverter-driven commercial PMSM instrumented with in situ FBG sensors monitoring phase winding hot spots is then used to evaluate the efficacy of the proposed diagnostic scheme. It is shown that unambiguous diagnosis and severity trending of winding open-circuit faults is enabled by the use of in situ FBG sensors. A comparison with conventional fault diagnostic technique utilizing current signal sensing and analysis is also reported, indicating the considerable advantages of the proposed monitoring scheme employing FBG sensors.

In-situ temperature near the chips in a multiple-chip power semiconductor module is important information for thermal design validation, condition monitoring and overheat protection. Due to the compact packaging structure and clamping force, online detection of the temperature is challenging for a press pack module. This study attempts to achieve measurement by integrating optical Fiber Bragg Grating (FBG) sensors into the device. Different integration schemes are examined using experiments and theoretical analysis to show the effects on the transient performance. It is shown that FBG sensors with plate housing can effectively indicate the transient temperature ripple in normal converter operation, owing to the fast dynamic response of a small size sensor. The package integration has only minimal effects on the original distributions of electrical, thermal and mechanical quantities within the power module.

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads.

This paper reports a study of rotor partial demagnetisation fault induced unbalanced magnetic pull (UMP) effects in the current and torque signals of a surface-mounted synchronous PM machine, with a view to evaluating their diagnostic application potential. An Finite Element Analysis (FEA) model of the test machine is first used to analyse the effects of rotor fault on the torque signal, and characterise the fault induced UMP effects. An experimental study is then performed on a purpose-built laboratory test rig for different demagnetisation fault severities in a number of operating points in the nominal operating range. The experimental results are used to explore the practical manifestation of the fault induced UMP related effects in the line current and rotor torque signals.

This paper presents a novel study of the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs) for the identification of rotor electrical faults. The aim is to advance the understanding of diagnostic information obtainable from the readily available DFIG controller signals. Analytical equations defining the controller signals possible spectral contents are derived to enable characterization of spectral signatures and their correlation to operating conditions and rotor faults. The equations are verified in a DFIG harmonic model study and also validated by undertaking a range of experiments on a laboratory DFIG test-rig. It is shown that the calculated, simulated and experimental results are in good agreement with regards to representing fault induced signatures in the examined DFIG controller signals spectra. Furthermore, it is shown that wide-band rotor electrical fault related spectral signatures in the controller signals carry considerable diagnostic potential for recognition of rotor electrical faults. UK Engineering and Physical Sciences Research Council HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms Consortium [EP/P009743/1] This work was supported by the UK Engineering and Physical Sciences Research Council HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms Consortium under Grant EP/P009743/1. WOS:000599507600057 2-s2.0-85098276892

This article presents a novel study of stator electrical faults detection using the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs). The possible wide-band spectral content of the controller signals are first defined with a set of analytical equations in order to characterize the wide-band spectral signatures and their correlation to operating conditions including stator electrical faults. A DFIG harmonic model and laboratory test-rig are used to validate the derived equations. The calculated, simulated and experimental results are in good agreement. Furthermore, it is shown that the wide-band stator electrical fault related spectral signatures carry considerable diagnostic potential for recognition of stator electrical faults, and therefore, can potentially be used to advance controller embedded fault detection techniques. U.K. Engineering and Physical Sciences Research Council (EPSRC) HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms ConsortiumUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P009743/1] This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms Consortium under Grant EP/P009743/1. Paper no. TEC-00044-2020. WOS:000652806800022 2-s2.0-85107015224

Data-driven wind generator condition monitoring systems largely rely on multi-stage processing involving feature selection and extraction followed by supervised learning. These stages require expert analysis, are potentially error-prone and do not generalize well between applications. In this paper, we introduce a collection of end-to-end Convolutional Neural Networks for advanced condition monitoring of wind turbine generators. End-to-end models have the benefit of utilizing raw, unstructured signals to make predictions about the parameters of interest. This feature makes it easier to scale an existing collection of models to new predictive tasks (e.g., new failure types) since feature extracting steps are not required. These automated models achieve low Mean Squared Errors in predicting the generator operational state (40.85 for Speed and 0.0018 for Load) and high accuracy in diagnosing rotor demagnetization failures (99.67%) by utilizing only raw current signals. We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. We further discuss existing options for understanding the decision process behind the predictions made by the models.

IEEE Industrial Electronics Society (IES);The Institute of Electrical and Electronics Engineers (IEEE) 2020 International Conference on Electrical Machines, ICEM 2020 -- 23 August 2020 through 26 August 2020 -- -- 165584 with the continuous increase in wind turbine generator size the monitoring and diagnosis of drive train shaft misalignment is of growing importance, as this condition contributes to as much as 30% of turbine's downtime. Machine current signature analysis has been widely investigated in conventional machinery as a non-invasive diagnostic tool for shaft misalignment, via spectrum analysis of the stator current signal. This paper reports a practical case study of misalignment fault signature manifestation in the doubly-fed induction generator (DFIG) controller signals spectra, with a view to assessing the feasibility of low cost and non-invasive controller signal analysis based misalignment diagnosis. The study employs a laboratory test rig to undertake a series of tests to analyze the sensitivity of controller signals to a specific angular misalignment condition, and hence evaluate and characterize the manifestation of its spectral signatures. © 2020 IEEE. EP/P009743/1; Engineering and Physical Sciences Research Council, EPSRC ?This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms Consortium under grant EP/P009743/1.) Y. Wang, A. Mohammed and S. Djurovi are with University of Manchester, School of Electrical and Electronic Engineering, Manchester, U.K. (email: yingzhao.wang@manchester.ac.uk) N .Sarma is with Duzce University, Department of Electrical and Electronic Engineering, Duzce, Turkey. 2-s2.0-85098643569

This article reports the use of fiber Bragg grating (FBG) sensors for electric machines’ in-service bearing condition monitoring. The proposed method enables simultaneous extraction of thermal and mechanical information on the bearing operational status for health monitoring and fault diagnosis purposes. The work first reports the instrumentation, calibration and the measurements interpretation methods of an FBG array sensor fitted to the drive-end bearing of an operating inverter driven induction motor. Experimental tests are then undertaken on a purpose build test rig to evaluate the proposed in-situ sensing scheme's performance under healthy and faulted bearing conditions. The results demonstrate that the measurements acquired by individual FBG sensors fitted in the bearing architecture contain concurrent thermal and mechanical information, which can be clearly differentiated to enable understanding of the examined motor bearing's thermal only and mechanical only operating conditions. Furthermore the in-situ measurements obtained in fault conditions are shown to contain clearly defined time and frequency domains fault signatures, which can enable unambiguous fault diagnosis and trending.

This paper reports a novel thermal monitoring scheme for on-line internal temperature measurement in low-voltage random wound machine stator windings. The scheme is based on utilizing electrically nonconductive and electromagnetic interference immune fiber optic sensing technology embedded in close proximity of hot spots of interest to create a robust distributed thermal monitoring network within the machine windings. The key design and implementation features of the proposed system are presented and applied on a prototype mains-fed induction motor. The on-line thermal monitoring performance is examined in a number of typical continuous and periodic running duty tests, as defined by the relevant IEC standards for rating and performance of rotating electrical machines. It is shown that the presented scheme has the potential to provide competent on-line measurement of critical machine thermal hot spots that are largely beyond the effective reach of conventional thermal monitoring solutions. Furthermore, the proposed scheme underpins a higher fidelity understanding of the distribution and propagation of winding thermal stress, demonstrated by the experimental analysis reported in the paper.

Sub-Synchronous Interaction (SSI), caused by the increasing penetration of inverter connected nonsynchronous generation, is posing a serious threat on power system stability. As the sub-/inter-harmonics emerging during SSI are featured by time-varying frequencies, concerns regarding the accuracy and the robustness of existing methods for monitoring oscillations of such phenomenon have been raised. This paper proposes a novel two-stage numerical method for monitoring the sub-harmonics, harmonics and inter-harmonics during SSI events. In the first stage, the model order of the estimation is selected using the proposed Adaptive Model Order Selection (AMOS) scheme, which is achieved through singular value decomposition. In the second stage, a linear prediction-based parameter estimation approach with high accuracy and strong robustness is used to estimate the signal parameters of the sub-harmonics, harmonics and inter-harmonics. By merging the above two stages, a novel and effective numerical method is obtained, namely the Adaptive Linear Prediction-based Parameter Estimation (ALPPE) method. In addition, an estimation calibration is implemented to increase the estimation accuracy. The proposed ALPPE method has then been tested using computer simulated signals and field measured data from an actual SSI event. The results of the tests confirmed the ALPPE method's high accuracy, adaptability, processing speed and immunity to noise.

This paper presents the development of a doubly fed induction machine (DFIG) harmonic model in matlab/Simulink, which is used to examine the spectral content of DFIG controller signals and improve the understanding of their behavior and spectral nature. The reported DFIG harmonic model has the capability of representing the effects of higher order time and space harmonics and thus, allows detailed analysis of the controller signals embedded spectral effects. The model consists of a wound rotor induction machine (WRIM) harmonic model coupled with a stator flux oriented controller model. The WRIM space harmonic effects are represented using the conductor distribution function approach to enable the calculation of winding inductances as a harmonic series. In addition, analytical expressions are derived to define the possible spectral content in the controller signals of DFIGs. Both the reported DFIG harmonic model and the analytical expressions are validated by comparison with measurements taken from a purpose built vector-controlled DFIG laboratory test rig. The findings confirm the capability of the developed DFIG harmonic model in representing the controller signals embedded spectral effects, as well as the accuracy of the reported analytical expressions, and enables a much improved understanding of the spectral nature of the DFIG controller signals. [EP/L014106/1] This work was supported by theUKEPSRC SUPERGENWind Hub under Grant EP/L014106/1. Paper no. TSTE-00333-2018. WOS:000522438600015 2-s2.0-85082589664

This study explores the potential for using FBG strain sensing to enable recognition of the shaft misalignment condition in electric machine drivetrains through observation of machine frame distributed relative strain. The sensing principles, design and installation methods of the proposed technique are detailed in the paper. The scheme was applied on a purpose built wind turbine generator representative laboratory test rig and its performance evaluated in an extensive experimental study involving a range of healthy and misaligned shaft operating conditions. The obtained experimental data demonstrate the reported method's capability to enable recognition of generator shaft misalignment conditions and thus its health monitoring. Finally, it is shown that the thermal variation of the generator frame structure inherent to its operation, combined with the FBG sensor intrinsic thermo-mechanical cross sensitivity, has no detrimental impact on the fidelity and usability of the observed strain measurements. U.K. Engineering and Physical Sciences Research Council HOME-Offshore: Holistic Operation and Maintenance for Energy From Offshore Wind Farms ConsortiumUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P009743/1] This work was supported by the U.K. Engineering and Physical Sciences Research Council HOME-Offshore: Holistic Operation and Maintenance for Energy From Offshore Wind Farms Consortium under Grant EP/P009743/1. WOS:000545717900038 2-s2.0-85088650688

This article proposes a random wound stator winding thermal monitoring scheme for permanent-magnet synchronous motors (PMSMs) utilizing an end-winding embedded, ring-shaped, fiber Bragg grating (FBG) thermal sensing array. The scheme enables in situ measurement of winding distributed thermal conditions, which is key to their effective health diagnosis and protection. It is designed to provide a thermal sensing point per each individual end-winding coil-end span and thus enable monitoring of the entire winding structure. This was achieved by utilizing FBG thermal sensing multiplexing in a ring-shaped sensor array inserted into the end-winding assembly to ensure desired in situ placement of separate sensing elements. The scheme was implemented on an inverter-driven PMSM and its performance examined in tests under healthy and faulted winding conditions. The results demonstrate the capability of effective monitoring of healthy windings distributed thermal status and that of unambiguous identification of localized overheating originating from winding fault, providing monitoring functionality that is largely unattainable by conventional thermal sensing techniques. Finally, the proposed scheme also enables straightforward advanced graphical visualization of the windings’ thermal status and, hence, a more effective diagnostic interpretation of thermal data to extract knowledge on locations of increased thermal stress.

This paper examines the feasibility of dedicated 3-D printed rigs use for enabling effective build and magnetisation of PM segments and controlled PM rotor assembly of a surface mounted PM rotor design. To this end a commercial surface mounted PM rotor design has been rebuilt and modified to allow for representation of a range of different PM magnetisation conditions. It is shown that suitable geometry assembly rigs made of electrically and magnetically passive material can be rapidly prototyped using 3D printing and applied to enable efficient PM segment build and magnetisation in a desired layout, as well as controlled surface mounted PM rotor assembly.

This paper reports a feasibility study of fibre Bragg Grating (FBG) sensing application for simultaneous extraction of thermal and mechanical information on electric machine bearings operational status for condition monitoring purposes from the same optical sensing head. The underlying aim is to examine the potential of using the readings from a single FBG head embedded in the bearing structure to infer information on both thermal and mechanical operating conditions observed at sensor location. To this end, experiments were undertaken on an FBG instrumented drive-end bearing of an operating inverter driven squirrel cage induction motor. Key features of the in-situ FBG sensor design, installation and calibration procedures along with the analysis of the obtained experimental measurements are reported. It is shown that the acquired measurements contain concurrent thermal and mechanical information that can be distinguished to enable understanding of the examined motor bearing thermal only and mechanical only operating conditions.

This paper reports an experimental feasibility study of the potential for using Fibre Bragg Grating (FBG) thermal sensing technology for electrical fault detection in random wound coils. The study is performed on prototype test coils housed in a purpose built steel core section. The FBG thermal sensors are embedded between copper conductors in multiple positions within the coil structure and in close proximity of the known hot spots of interest. The examined coils are designed to enable emulation of hot spots representative of those that would be expected at the outset of winding fault events. A series of experiments were separately conducted in order to characterise the potential of coil internal thermal stress monitoring to provide recognition of a developing winding fault. To this end, winding fault scenarios with different severity levels of inter-turn fault are experimentally examined. The reported findings demonstrate the potential of the proposed in-situ thermal sensing scheme to enable monitoring and recognition of coil internal thermal stress induced at various stages of emulated electrical fault.

This work reports a Finite Element Analysis (FEA) model study of the stator current spectra of a commercial permanent magnet (PM) AC machine operating with rotor PM demagnetisation faults. A range of PM uniform and local demagnetisation fault scenarios at different severity conditions are examined. The aim of this study is to investigate the extent to which different modes of PM demagnetisation could be distinguished by inspecting the spectral signature patterns these generate in the stator current signal. To this end, a generalised mathematical characterisation of PM fault spectral signature in the stator current is examined and a 2-D FEA software used to establish a model of the studied PM machine design. The FEA model was employed to predict the current signal spectral signature patterns of a range of different PM demagnetisation faults. These are then correlated with those arising from the derived expressions to examine whether the predicted and expected spectral trends are in agreement and whether their observation as the potential to provide diagnostic information.

The utilisation of conventional industrial converters for development of doubly-fed induction generator (DFIG) test facilities poses an attractive prospect as it would provide proprietary commercial protection and functionality. However, standard commercial converters present significant challenges in attainable DFIG operational capability. This is due to the fact that they are designed for execution of a limited set of pre-programmed common control modes. They typically do not cater for execution of complicated stator flux-oriented vector control (SFOC) schemes required for DFIG drive control. The research work presented in this study reports a methodology that enables effective implementation of SFOC on industrial converters through a dedicated external real-time platform and a velocity/position communication module. The reported scheme is validated in laboratory experiments on an experimental DFIG test-rig facility. The presented principles are general and are therefore applicable to conventional DFIG drive architectures utilising standard industrial converters.

This paper investigates the spectral effects that stator supply harmonics can induce in wound rotor and doubly-fed induction generators. The purpose of this study is to clarify the nature and the origin of the reported significant inter harmonic effects in these machines. To this end, the authors derive analytical expressions that define the spectral signature frequencies of individual supply harmonics in the current and torque signals. The expressions are first validated through comparison with the spectra calculated using a harmonic time-stepped machine model and then by spectral measurements on a laboratory test rig comprising a 30 kW machine excited by a programmable power supply unit. It is shown that the presence of supply harmonics can result in multiple and variable wide-band interharmonic effects in the current and torque signals. Finally, the interharmonic current emissions for the wound rotor and the doubly-fed mode of operation are experimentally quantified using the relevant IEC standard indices.

Power system loads are one of the crucial elements of modern power systems and, as such, must be properly modelled in stability studies. However, the static and dynamic characteristics of a load are commonly unknown, extremely nonlinear, and are usually time varying. Consequently, a measurement-based approach for determining the load characteristics would offer a significant advantage since it could update the parameters of load models directly from the available system measurements. For this purpose and in order to accurately determine load model parameters, a suitable parameter estimation method must be applied. The conventional approach to this problem favors the use of standard nonlinear estimators or artificial intelligence (AI)-based methods. In this paper, a new solution for determining the unknown load model parameters is proposed—an improved particle swarm optimization (IPSO) method. The proposed method is an AI-type technique similar to the commonly used genetic algorithms (GAs) and is shown to provide a promising alternative. This paper presents a performance comparison of IPSO and GA using computer simulations and measured data obtained from realistic laboratory experiments.

This work reports an improved estimation reliability wide range rotor speed estimation algorithm for scalar controlled wound rotor induction machines using spectral search of the stator power signal. An adaptive sliding window speed estimation method is proposed that monitors the value of supply frequency to identify the optimum boundaries of the power signal narrowband maximised by a tracked speed dependent power harmonic, while adjusting to nominal slip variation with supply frequency change characteristic of scalar controlled machines. Analytical expressions are derived that allow direct correlation of the surveyed spectral window boundaries to supply frequency. Once identified, the optimal power spectral narrowband enables supply frequency independent speed evaluation. The proposed method is underpinned by a dichotomous search based frequency tracking technique enabling improvement in the attainable estimation rate. The presented technique’s performance and limitations are assessed in steady-state and transient operation real-time experiments on two different scalar controlled 7.5 kW machines. The scheme is shown to provide competent real-time speed estimation in a wide operating speed range for lower dynamics transients and steady-state operation.

This paper investigates wound rotor induction machine torque and vibration signals spectra for operation with and without a stator short-circuit or open-circuit winding fault. Analytical expressions that enable the healthy and faulty machine pulsating electromagnetic torque frequencies to be related to shaft speed are derived and validated for operating conditions of interest. A coupled-circuit machine model is used to investigate the healthy and faulty electromagnetic torque signal. Shaft torque and stator frame vibration are measured on a laboratory test rig comprising a 30 kW wound rotor induction machine. It is shown that the existence of a stator winding inherent electrical unbalance or that arising from fault gives rise to a range of pulsating torque frequencies that are transmitted to the machine frame and can be detected in the measured vibration signal. The magnitudes of the resulting vibration components are demonstrated to be largely determined by the unbalance severity and the mechanical system response. The presented experimental results clearly validate the analytical and simulation analysis for the operating range of the investigated industrial machine design.

This paper investigates the application of Fibre Bragg sensing for monitoring of induction machine frame strain. The underlying purpose is to examine the feasibility of frame surface attached fibre optic sensing to be used for condition monitoring purposes, i.e. for recognition of signatures characteristic of healthy and/or faulty machine operating conditions. To this end an experimental study is reported that compares the fibre optic monitored circumferential strain signal spectral content with that of synchronously monitored frame acceleration and shaft torque signals on a laboratory induction machine operating with and without electrical fault. It is shown that the signature of electrical unbalance and fault, as well as that of mechanical effects on the shaft can be effectively recognised in the frame strain signal monitored by the fibre optic strain sensor.