Vibration analysis via neural network inverse models to determine aircraft engine unbalance condition

The most important things in the dynamic research of industrial robots are the vibration behavior, the transfer function and the vibration power spectral density between some of the robot joints and components. In the world this research is made without the assisted research. In each of the study cases in this paper was used the proper virtual Fourier analyzer and was presented one new method of the assisted vibration analysis. With this research it is possible the optimal choosing the base modulus type to avoid the frequencies from the robot spectrum. In the manufacturing systems, the most important facts are the vibration behavior of the robot, the compatibility with some other components of the system. All the VI where achieved in the LabVIEW soft 8.2 version, from National Instruments, USA. This method and the created virtual LabVIEW instrumentation are generally and they are possible to apply in many other dynamic behavior research.

Download Full-text

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

Measurement Science and Technology ◽

10.1088/1361-6501/ac4b8f ◽

2022 ◽

Author(s):

Massine GANA ◽

Hakim ACHOUR ◽

Kamel BELAID ◽

Zakia CHELLI ◽

Mourad LAGHROUCHE ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Induction Motor ◽

Vibration Analysis ◽

Low Cost ◽

Piezoelectric Sensor ◽

Integrated System ◽

Easy Access ◽

Non Invasive ◽

Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Download Full-text

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.2179465 ◽

2005 ◽

Vol 128 (4) ◽

pp. 773-782 ◽

Cited By ~ 13

Author(s):

H. S. Tan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Fourier Transforms ◽

Aircraft Engine ◽

Fault Classification ◽

Noise Robustness ◽

Fault Diagnostics ◽

Multiple Faults ◽

Hidden Layer

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

Download Full-text