This article presents a novel approach to the diagnosis of unbalanced faults in a trolling motor under stationary operating conditions. The trolling motor being typically of that used as the propulsion system for an unmanned surface vehicle, the diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and a time-delayed neural network for fault classification. The time-delayed neural network classifies between healthy and faulty conditions of the trolling motor by analysing the stator current and vibration. To overcome feature redundancy, which affects diagnosis accuracy, several feature reduction methods have been tested, and the orthogonal fuzzy neighbourhood discriminant analysis approach is found to be the most effective method. Four faulty conditions were analysed under laboratory conditions, where one of the blades causing damage to the trolling motor is cut into 10%, 25%, half and then into full to simulate the effects of propeller blades being damaged partly or fully. The results obtained from the real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately.
BerririHNaouarWSlamaI. Easy and fast sensor fault detection and isolation algorithm for electrical drives. IEEE T Power Electr2012; 27(2): 490–499.
2.
SerdioFLughoferEPichlerK. Residual based fault detection using soft computing techniques for condition monitoring at rolling mills. J Inform Sci2014; 259: 304–320.
3.
GyftakisNSpyropoulosVKappatouC. A novel approach for broken bar fault diagnosis in induction motors through torque monitoring. IEEE T Energy Conver2013; 28: 267–277.
4.
MurpheyLMasrurAZhihangC. Model-based fault diagnosis in electric drives using machine learning. IEEE/ASME T Mech2006; 11(3): 290–303.
5.
LemosACaminhasWGomideF. Adaptive fault detection and diagnosis using an evolving fuzzy classifier. J Inform Sci2013; 220: 64–85.
6.
ZhengYFangHWangH. Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays. IEEE T Syst Man Cy B2006; 36(4): 924–929.
7.
WenX. A hybrid intelligent technique for induction motor condition monitoring. PhD Thesis, Institute of Industrial Research, University of Portsmouth, Portsmouth, 2011.
8.
DingX. Model based fault diagnosis techniques: design schemes algorithms and tools. 1st ed.Berlin: Springer Verlag, 2013.
9.
LughoferE. Evolving fuzzy system methodologies, advanced concepts and applications, studies in fuzziness and soft computing, vol. 266. Berlin, Heidelberg: Springer, 2011.
10.
LughoferE. On-line assurance of interpretability criteria in evolving fuzzy systems achievements. J Inform Sci2013; 251: 22–46.
11.
GactoJAlcalaRHerreraF. Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inform Sciences2011; 181(20): 4340–4436.
12.
MagneS. Simplification and reduction of fuzzy rules. In: Studies in fuzziness and soft computing modelling, vol. 128. Springer Berlin Heidelberg, 2003, pp.278–302.
13.
LughoferEJeanBAmmerS. On-line elimination of local redundancies in evolving fuzzy systems. Evol Syst2011; 2(3): 165–187.
AhmadiHSakhaeiBLabbafiR. Support vector machine and K-nearest neighbour for unbalanced fault detection. J Qual Mainten Eng2014; 20(1): 65–75.
16.
XuhongWYigangH. Diagonal recurrent neural network based on-line stator winding turn fault detection for induction motors. In: Proceedings of the IEEE eighth international conference on electrical machines and systems, Nanjing, 2005, pp. 2266–2269.
17.
SerdioFLughoferEPichlerK. Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformation. Inform Fusion2014; 20: 272–291.
18.
SeyedMAbdesslemDDavodK. Fault detection in 3-phase Traction Motor Using Artificial Neural Networks. In: IEEE conference on transportation electrification, Dearborn, 2012, pp.1–6.
19.
WidodoAYangS. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst Appl2007; 33(1): 241–250.
20.
ChiangHRussellLBraatzD. Fault detection and diagnosis in industrial systems (Advanced textbooks in control and signal processing). 1st ed.London: Springer, 2001.
21.
FuenteJ. Fault detection in a wastewater treatment plant based on neural networks and PCA, In: 20th Mediterranean Conference on Control & Automation, Barcelona, 3–6, July 2012, pp. 758–763.
22.
OdgaardFLinBJorgensenS. Observer and data-driven-model-based fault detection in power plant coal mills. IEEE T Energy Conver2008; 23(2): 659–668.
23.
KhushabaNAl-AniAAl-JumailyA. Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control. IEEE T Bio-Med Eng2010; 57(6): 1410–1419.
24.
PichlerKPichlerMLughoferE. On the robustness of fault detection in reciprocating compressor valves. In: proceeding of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, 5–8 October 2014, pp.2733–2738. New York: IEEE.
25.
MitomaTWangHChenP. Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network. Comput Ind Eng2008; 55(4): 783–794.
26.
GoharriziYSepehriN. A wavelet-based approach to internal seal damage diagnosis in hydraulic actuators. IEEE T Ind Electron2010; 57(5): 1755–1763.
27.
SeshadrinathJSinghBPanigrahiBK. Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE T Power Electr2014; 29(2): 936–945.
28.
YanRGaoRXChenX. Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process2014; 96: 1–15.
29.
AntoninoJAviyenteSStrangasG. Scale invariant feature extraction algorithm for the automatic diagnosis of rotor asymmetries in induction motors. IEEE T Ind Inform2013; 9(1): 100–108.
30.
AntoninoJRieraMRogerJ. Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Appl Comput Harmon A2006; 21(2): 268–279.
31.
LiWMontiAPonciF. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE T Instrum Meas2014; 63(11): 2651–2665.
32.
AlokSKuldipKGodfreyC. Class-dependent PCA, MDC and LDA: a combined classifier for pattern classification. Pattern Recogn2006; 39(7): 1215–1229.
33.
PrietoDCirrincioneGEspinosaG. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE T Ind Electron2013; 6(8): 3398–3407.
34.
DelgadoMCirrincioneGGarcia EspinosaA. Bearing faults detection by a novel condition monitoring scheme based on statistical-time features and neural networks. IEEE T Ind Electron2013; 60(8): 3398–3407.
35.
JolliffeT. Principal component analysis. 2nd ed.Berlin, Heidelberg; New York: Springer, 2002.
