scholarly journals Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures

2022 ◽  
Vol 11 (1) ◽  
pp. e14211125104
Author(s):  
Márcio Pereira Corrêa ◽  
Ayslan Cuzzuol Machado ◽  
João Inácio da Silva Filho ◽  
Dorotéa Vilanova Garcia ◽  
Mauricio Conceição Mario ◽  
...  
Keyword(s):  
Paraconsistent Logic ◽  
Failure Detection ◽  
Special Treatment ◽  
Complex Signals ◽  
Vibration Signals ◽  
Appropriate Treatment ◽  
Vibration Signature ◽  
Vibration Signatures ◽  
Annotated Logic ◽  
Current Risk

In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

Experiments on the extraction of blade vibration signature from the shaft torsional vibration signals

2003 ◽  
Vol 9 (2) ◽  
pp. 144-159 ◽  
Author(s):  
B.O. Al‐Bedoor ◽  
L. Ghouti ◽  
S.A. Adewusi ◽  
Y. Al‐Nassar ◽  
M. Abdlsamad
Keyword(s):  
Torsional Vibration ◽  
Blade Vibration ◽  
Vibration Signals ◽  
Vibration Signature

Enhancement of Turning Process Using Vibration Signature Analysis

2005 ◽  
Author(s):  
Marlon C. Batey ◽  
Hamid R. Hamidzadeh
Keyword(s):  
Manufacturing Process ◽  
Vibration Analysis ◽  
Manufacturing System ◽  
Cutting Tool ◽  
Operating Conditions ◽  
Turning Process ◽  
Analysis Problem ◽  
Frequency Domains ◽  
Vibration Signature ◽  
Vibration Signatures

Analytical and experimental vibration analyses are conducted for a lathe system to detect the possibility of faults and develop an accurate cutting process. The data acquisition system utilized for this purpose processes the analog input from the manufacturing system and displays the response in both the real time and frequency domains. The vibration signatures for different arrangements are recorded to determine the dynamic characteristics of the system which includes work pieces, tool, and lathe components. These vibration signatures were analyzed to determine cause of inaccuracy in the manufacturing process and the faulty components. In this study, two major problem causing sources were identified using vibration analysis for the system under different operating conditions. In addition to the identified problems, the phenomena of cutting tool chatter with various intensities was examined and recorded during testing. In this study the best possible operating conditions for a specific turning process were determined using vibration analysis. Problem causing components for several case studies (different speeds, feed rates, and tool lengths) were identified and guidelines for improving a typical manufacturing process were recommended.

Digital Analysis of the Vibration Signals Amplitude Spectrum Based on Fourier Processing of the Binary-Sign Analog-Stochastic Quantization Result

2019 ◽  
Vol 20 (12) ◽  
pp. 723-731
Author(s):  
V. N. Yakimov ◽  
V. I. Batyschev ◽  
A. V. Mashkov
Keyword(s):  
Harmonic Analysis ◽  
Computational Efficiency ◽  
Amplitude Spectrum ◽  
Experimental Studies ◽  
Analytical Calculation ◽  
Frequency Resolution ◽  
Stochastic Quantization ◽  
Complex Signals ◽  
Workload Analysis ◽  
Vibration Signals

The article is devoted to the problem of developing a digital algorithm for operational harmonic analysis of complex vibration signals. The basis for solving this problem was the generalized equation of statistical measurements, which defines the measurement procedure as the sequential execution of interrelated measurement and computational transformations. During the development of the algorithm, special attention is paid to analog-to-digital conversion because it directly affects the computational efficiency of digital procedures for obtaining the final result. As such a conversion, the use of binarysign analog-stochastic quantization is justified, which allows performing two-level quantization without systematic error regardless of the statistical properties of the analyzed signals. The discrete-event model of the binary-sign analog-stochastic quantization result allowed for the analytical calculation of integration operations in the transition to estimating the amplitude spectrum in digital form. As a result, the developed algorithm of harmonic analysis does not require performing digital multiplication operations typical for classical algorithms, which are based on the calculation of the direct discrete Fourier transform. The execution of the algorithm is reduced to the implementation of the addition and subtraction arithmetic operations of the cosine-function values in the time moments determined by the result of the binary-sign analogue-stochastic quantization. The exclusion of digital multiplication operations provided an increase in the computational efficiency of amplitude spectrum estimation. Laboratory studies of the developed algorithm were carried out using simulation modeling. The simulation results showed that the algorithm allows calculating estimates of the amplitude spectrum of complex signals with high accuracy and frequency resolution in the presence of additive noise. In real conditions, the testing of the developed algorithm was carried out during bench studies of the operational status of the MAZ-206067 bus, designed for the transportation of passengers on urban and suburban routes of average workload. Analysis of the results of experimental studies confirmed the possibility of using the algorithm as part of the diagnosability provision for operational monitoring of vibration signals in a complex noise environment.

scholarly journals Comparative structural vibration analysis of machinery and GFRP with Al7075

2018 ◽  
Vol 144 ◽  
pp. 01009
Author(s):  
K. Vinoth Kumar ◽  
T. G. Loganathan ◽  
A. Bharath ◽  
B. Shyam Sundar ◽  
K. K. Abishek
Keyword(s):  
Fourier Transforms ◽  
Fast Fourier Transforms ◽  
Structural Vibration ◽  
Vibrational Properties ◽  
Frequency Spectra ◽  
Metal Fabrication ◽  
Current Scenario ◽  
Vibration Signature ◽  
Vibration Signatures ◽  
A Company

The current scenario of the industries is that the major losses in efficiency of a machine are due to vibration and friction. To reduce the detrimental effects of vibration we need to decrease the frequency and amplitude of vibration or completely eliminate vibration. To do that one must quantify vibration that already occurs in machinery and structural components. Which is the aim of this paper. The intention of the paper is to obtain and characterize the vibration signature of equipment used in a company and composite material. We have designed a setup to vibrational properties composites, vibrational signature of industrial equipment .To study vibration properties, micro-electrical mechanical systems (MEMS) based accelerometers are used to measure acceleration of the material about the datum when displaced. The data obtained is processed in MATLAB using ARDUINO relayed to computer to convert the data to frequency spectra using Fast-Fourier transforms (FFT). We ultimately compared the vibrational properties of two lathes used at a metal fabrication plant operating at different Conditions and quantified the vibration results using Fast Fourier Transforms (FFT) algorithm. The vibration signatures of a composite is studied along with which various properties like Damping Coefficient, Free Vibration, GFRP, Natural Frequency applications are studied.

Detection of Carrier-Plate Cracks Using Vibration Spectra

2007 ◽  
Author(s):  
Romano Patrick ◽  
Al Ferri ◽  
George Vachtsevanos
Keyword(s):  
Experimental Data ◽  
Mathematical Analysis ◽  
Planetary Gear ◽  
Vibration Signals ◽  
Vibration Spectra ◽  
Vibration Sensors ◽  
Vibration Signature ◽  
Frequency Components ◽  
Planet Gear ◽  
Gear Systems

This paper examines the problem of identifying cracks in planetary gear systems through use of vibration sensors on the stationary gearbox housing. In particular, the effect of unequal spacing of planet gears relative to the rotating carrier plate on various frequency components in the vibration spectra is studied. The mathematical analysis is validated with experimental data comparing the vibration signature of helicopter transmissions operating either normally or with damage leading to shifts in the planet gear positions. The theory presented is able to explain certain features and trends in the measured vibration signals of healthy and faulty transmissions. The characterization offered may serve as a means of detecting damage in planetary gear systems.

ANN Based Fault Classification of High Speed Ball Bearings

2009 ◽  
Author(s):  
Aashish Bhatnagar ◽  
P. K. Kankar ◽  
Satish C. Sharma ◽  
S. P. Harsha
Keyword(s):  
High Speed ◽  
Fault Analysis ◽  
Fault Classification ◽  
Wavelet Filter ◽  
Vibration Signals ◽  
Training Time ◽  
Outer Race ◽  
Key Issues ◽  
The Time Domain ◽  
Vibration Signatures

In the rotating machines, maintenance of the high speed operated bearings is the major problem and is one of the key issues due to excessive vibrations. Hence, the vibration signatures can be used as a feature for the fault diagnosis. This paper presents the Artificial Neural Networks (ANN) based fault analysis, which is used to classify various known faults using the features extracted from the vibration signals. The vibration signals from the piezoelectric accelerometers are being measured for the following conditions — No defect (NOD), Outer race defect (ORD), Inner race defect (IRD), Ball fault (BF) and Combination of above (COMB). The features are extracted from the time domain using the statistical method. These features are filtered using wavelet filter & kernel filter and compiled as the input vectors. The multilayer neural network is trained by these input vectors. The training and testing results show that wavelet and kernel filter can be effective tool in the diagnosis of ball bearing faults using ANN. Results obtained from the ANN predict that the wavelet filter provides good accuracy with reduction in the training time.

Gear condition monitoring by a new application of the Kolmogorov—Smirnov test

2001 ◽  
Vol 215 (6) ◽  
pp. 653-661 ◽  
Author(s):  
F A Andrade ◽  
I I Esat ◽  
M N M Badi
Keyword(s):  
Condition Monitoring ◽  
Spur Gear ◽  
Processing Technique ◽  
Reference Distribution ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Vibration Signature ◽  
Kolmogorov Smirnov ◽  
Vibration Signatures ◽  
Ks Test

This paper introduces a new technique for the vibration condition monitoring of a set of spur gears. This technique, the Kolmogorov—Smirnov (KS) test, is based on a statistical comparison of two vibration signatures, which tests the ‘null hypotheses that the cumulative density function (CDF) of a target distribution is statistically similar to the CDF of a reference distribution’. In practice, the KS test is a time-domain signal processing technique that compares two signals and returns the likelihood that the two signals are statistically similar (i.e. have the same probability distribution function). Consequently, by comparing a given vibration signature with a number of template signatures for known gear conditions, it is possible to state which is the most likely condition of the gear under analysis. It must be emphasized that this is not a moment technique as it uses the whole CDF instead of sections of the CDF. In this work, the KS test is applied to the specific problem of direct spur gear condition monitoring. It is shown that this test not only successfully identifies the condition of the gear under analysis (brand new, normal, faulty and worn out), but also gives an indication of the advancement of the crack. Furthermore, this technique identifies cracks that are not identified by popular methods based on the statistical moment and/or time-frequency (TF) analysis and the vibration signature. This shows that, despite its simplicity, the KS test is an extremely powerful method that effectively classifies different vibration signatures, allowing for its safe use as another condition monitoring technique.

scholarly journals Analysis of the sound signal to fault detection of bearings based on Variational Mode Decomposition

2021 ◽  
Vol 942 (1) ◽  
pp. 012020
Author(s):  
Hamid Shiri ◽  
Jacek Wodecki
Keyword(s):  
Fault Detection ◽  
Damage Detection ◽  
Acoustic Signal ◽  
Noise Signal ◽  
Special Treatment ◽  
Variational Mode Decomposition ◽  
Test Rig ◽  
Vibration Signals ◽  
Vibration Data ◽  
Mode Decomposition

Abstract Damage detection in rotating machines is well established for vibration signals. Unfortunately, there are situations, where usage of vibration is not possible. Then, acoustic signal could be used instead. Unfortunately, usually acoustic signal are more noisy and require special treatment for obtain successful damage detection. In the paper we propose to use Variational mode decomposition (VMD) to omit noise for finding de-noise signal. We use vibration data to validate acoustic signal based procedure. The experiment was done on test rig with damaged bearings.

scholarly journals Application of Shannon Entropy in the Construction of a Paraconsistent Model of the Atom

2020 ◽  
Author(s):  
João Inácio da Silva Filho
Keyword(s):  
Shannon Entropy ◽  
Quantum Physics ◽  
Energy Levels ◽  
Oil Quality ◽  
Paraconsistent Logic ◽  
Main Property ◽  
Wave Functions ◽  
Logarithmic Function ◽  
Proposed Model ◽  
Annotated Logic

In this work, we present a model of the atom that is based on a nonclassical logic called paraconsistent logic (PL), which has the main property of accepting the contradiction in logical interpretations without the conclusions being annulled. The proposed model is constructed with an extension of PL called paraconsistent annotated logic with annotation of two values (PAL2v), which is associated with an interlaced bilattice of four vertices. We use the logarithmic function of the Shannon entropy H(s) to construct the paraconsistent equations and thus adapt a probabilistic model for representations in quantum physics. Through analyses of the interlaced bilattice, comparative values are obtained for some of the phenomena and effects of quantum mechanics, such as superposition of states, quantum entanglement, wave functions, and equations that determine the energy levels of the layers of an atom. At the end of this article, we use the hydrogen atom as a basis of the representation of the PAL2v model, where the values of the energy levels in six orbital layers are obtained. As an example, we present a possible method of applying the PAL2v model to the use of Raman spectroscopy signals in the detection of lubricating mineral oil quality.

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