Research Article Open Access Volume 7 • Issue 4 • 1000273 J Electr Electron Syst, an open access journal ISSN: 2332-0796 Open Access Research Article Journal of Electrical & Electronic Systems J o u r n a l o f E l e c tr i c a l & E l e c t r o n i c S y s t e m s ISSN: 2332-0796 Baradieh, J Electr Electron Syst 2018, 7:4 DOI: 10.4172/2332-0796.1000273 *Corresponding author: Baradieh K, Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Saudi Arabia, Tel: 966533591540; E-mail: Khalid.baradia@outlook.com Received July 27, 2018; Accepted September 03, 2018; Published September 10, 2018 Citation: Baradieh K, Al-Hamouz Z, Abido M (2018) ANN Based Broken Rotor Bar Fault Detection in LSPMS Motors. J Electr Electron Syst 7: 273. doi: 10.4172/2332- 0796.1000273 Copyright: © 2018 Baradieh K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ANN Based Broken Rotor Bar Fault Detection in LSPMS Motors Baradieh K*, Al-Hamouz Z and Abido M Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Saudi Arabia Keywords: LSPMSM; Broken bar; Stator current signature; Fault; FEM; Coupled magnetic circuit; Winding function; JMAG; ANN; SVD Introduction Motors are ubiquitous in everyday life and have wide ranging applications, such as industrial, commercial and residential utilization. Statistics indicate that electrical motors account for about two-thirds of the total industrial power consumption in each society. Because of the unlimited number of electric motor applications, there are over 700 million motors of various sizes in operation across the world [1]. Induction motors constitute by far the largest portion of electrical motors in the market. However, motors that are more efcient began to appear as an alternative. In the last few years, “Line Start Permanent Magnet Synchronous Motor (LSPMSM)” emerged as a powerful candidate in the motor industry, and has been promoted for industrial, commercial and residential applications. Tis motor has many desirable features that will likely expand its market size. Terefore, LSPMSM is expected to replace the currently utilized motors, such as induction motors. On the other hand, it is well known that the AC motors are susceptible to many faults types such as broken rotor bars fault. Motor failures may result in catastrophic events, including production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs and wasted in the raw materials. For these reasons, studying the LSPMSM and fnding a reliable diagnostic and monitoring tool under broken bar fault condition is urgent need. Recently, monitoring machine faults has constituted an interest of research teams. Broken rotor bars are a common fault in AC machines. Dedicated diagnostic techniques and systems are demanded to detect an upcoming machine defect as early as possible. Consequently, there is an extensive body of work on the monitoring and detection techniques of the broken rotor bars in induction motors. For example, Hwang [2] proposed an algorithm for detecting the broken bar faults in induction motors based on the dimension order of the frequency signal, which was called Frequency Signal Dimension Order (FSDO). FSDO was used to analyze the stator current signal, as well as to estimate the fault type based on the resultant frequency. Te fault decision model analyzed the data derived from FSDO to decide whether there is a fault or not based on certain indices. Tis work was verifed by comparing simulation and experimental results pertaining to a 3-phase, squirrel cage induction motor. Te main disadvantage of this work was FSDO estimator, which gave a good performance for steady-state operations only. Carlos [3] demonstrated the efect of the broken bar fault on the stator current signature in the induction motor, and then used the “zero setting protection element” method to detect the occurrence Abstract Line Start Permanent Magnet Synchronous Motors (LSPMSMs) combine the high effciency of the permanent magnet synchronous motors (PMSM) with the ease of use, simplicity in design and high starting capability of the induction motors (IMs). Due to the rapidly growing usage of this relatively new motor, proposing a diagnostics method for broken rotor bar fault is necessity. In this paper, a diagnostics technique based on Artifcial Neural Network (ANN) was developed to detect the broken bars fault in LSPMSM using Singular Value Decomposition (SVD) in order to extract distinguishing features from the stator phase current. This distinguishing attributes were proposed to be the inputs to the built neural network. of the broken bar fault under diferent loading levels. Tis work has successfully detected the broken rotor bar fault under diferent loading levels with high accuracy. Another method for diagnostics of broken rotor bar in induction motors was introduced by Zarei et al. [4], the broken bars are monitored based on the artifcial neural network using “particle swarm optimization” in training process that work was performed in two stages, commencing with designing a flter to remove noise components from the faulty motor current. Te least squares algorithm was also used to fnd the flter coefcients. In the second stage, a neural network was trained to extract the fault classifcations. Te output of this network was utilized to classify the state of the motor into four types: healthy bar, cracked on the bar, one and two broken bars. Guo-Liang [5] proposed a method to diagnose the broken bar faults in induction motors based on “empirical decomposition method”. In that work, several intrinsic mode functions (IMF) were used to decompose and analyze the starting current, before applying Hilbert transform for frequency analysis. As a result of this work, and because of the symmetry in the rotor, broken bar fault caused a frequency component (1-2s)f, where s denotes slip and f pertains to source frequency. Using Hilbert transform, several researches attempted developing diagnostics methods for broken rotor bar in induction motors under no load conditions. For example, Aydin [6] utilized sliding window for several periods. Tat method was used to detect one or two broken bars only under no load and with a supply voltage greater than 260 V. Te entropy of the incoming data was calculated and compared with some threshold as the frst stage. In the second stage, the fault size was determined, i.e., the number of faulty bars. High accurate results were obtained using this method with little computation cost in short time, which were tested under limited conditions.