A Review on the Faults of Electric Machines Used in Electric Ships

Electric propulsion systems are today widely applied in modern ships, including transport ships and warships. The ship of the future will be fully electric, and not only its propulsion system but also all the other services will depend on electric power. The robust and reliable operation of the ship’s power system is essential. In this work, a review on the mechanical and electrical faults of electric machines that are used in electric ships is presented

Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives

A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner

Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

[EN] This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation experimental approach demonstrate the effectiveness of the proposed methodology.This work was supported in part by the Conselleria d'Educacio, Formacio i Ocupacio of the Generalitat Valenciana, in the framework of the "Ayudas para la Realizacion de Proyectos de I+D para Grupos de Investigacion Emergentes," project reference GV/2012/020.Georgoulas, G.; Tsoumas, IP.; Antonino-Daviu, J.; Climente Alarcón, V.; Stylios, CD.; Mitronikas, ED.; Safacas, AN. (2014). Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines. IEEE Transactions on Industrial Electronics. 61(9):4937-4946. https://doi.org/10.1109/TIE.2013.22841434937494661