•Journal•ISSN: 1070-9622
Shock and Vibration
About: Shock and Vibration is an academic journal. The journal publishes majorly in the area(s): Vibration & Finite element method. It has an ISSN identifier of 1070-9622. It is also open access. Over the lifetime, 4806 publication(s) have been published receiving 34549 citation(s).
Topics: Vibration, Finite element method, Nonlinear system, Fault (power engineering), Bearing (mechanical)
Papers published on a yearly basis
Papers
More filters
[...]
TL;DR: In this paper, a non-iterative frequency-domain parameter estimation method is proposed based on a weighted least-squares approach and uses multiple-input-multiple-output frequency response functions as primary data.
Abstract: Recently, a new non-iterative frequency-domain parameter estimation method was proposed. It is based on a (weighted) least-squares approach and uses multiple-input-multiple-output frequency response functions as primary data. This so-called "PolyMAX" or polyreference least-squares complex frequency-domain method can be implemented in a very similar way as the industry standard polyreference (time-domain) least-squares complex exponential method: in a first step a stabilisation diagram is constructed containing frequency, damping and participation information. Next, the mode shapes are found in a second least- squares step, based on the user selection of stable poles. One of the specific advantages of the technique lies in the very stable identification of the system poles and participation factors as a function of the specified system order, leading to easy-to-interpret stabilisation diagrams. This implies a potential for automating the method and to apply it to "difficult" estimation cases such as high-order and/or highly damped systems with large modal overlap. Some real-life automotive and aerospace case studies are discussed. PolyMAX is compared with classical methods concerning stability, accuracy of the estimated modal parameters and quality of the frequency response function synthesis.
572 citations
[...]
TL;DR: An implementation of deep learning algorithm convolutional neural network used for fault identification and classification in gearboxes using different combinations of condition patterns based on some basic fault conditions indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
Abstract: Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.
254 citations