Vibration Components Generated by Rotary Machinery

2021 ◽  
pp. 31-47
Author(s):  
Adam Jablonski
Keyword(s):  
Rotary Machinery

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Damping lateral vibrations in rotary machinery using motor speed modulation

2010 ◽  
Vol 329 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Nikolai N. Verichev ◽  
Stanislav N. Verichev ◽  
Vladimir I. Erofeyev
Keyword(s):  
Motor Speed ◽  
Lateral Vibrations ◽  
Speed Modulation ◽  
Rotary Machinery

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4017 ◽  
Author(s):  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Michał Pająk ◽  
Danijel Pavković
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Input Matrix ◽  
Accelerometer Signal ◽  
Rotary Machinery ◽  
Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

A Low Cost Velocity Measurement System Design for Rotary Machinery

2010 ◽  
Author(s):  
Cagatay Cakir ◽  
Hasan Koruk ◽  
Burak Ulas
Keyword(s):  
System Design ◽  
Measurement System ◽  
Velocity Measurement ◽  
Practical Problem ◽  
Low Cost ◽  
Measurement Systems ◽  
Rotary Machinery ◽  
Measurement Devices ◽  
Motorized Vehicles ◽  
Sufficient Precision

Today, as the importance of system automation increases, measurement systems become more and more important. Consequently, in many applications, from washing machines, motorized vehicles, robots to nuclear turbine reactors, velocity measurement is inevitable. In industry, velocity is widespreadly needed to be measured. Besides that researchers through the globe need such measurement devices in their studies. On the other hand, to be able to make a correct measurement, it may be needed to pay much on measuring equipments while the economical issue is sometimes the reason for the research does not continue on or even not begin. So, it has always been a practical problem for both industry and researchers not to be able to measure the rotating velocity of machinery with both sufficient precision and low cost. In this paper, a very low cost but still precise velocity measurement system design is introduced, explained and discussed. First, building up of the sensor circuit and basic components of the system are introduced. Then, data acquisition and signal processing of the system are explained. Finally, advantages of the system are discussed and some conclusions are given.

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