scholarly journals Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Jose Saucedo-Dorantes ◽  
Miguel Delgado-Prieto ◽  
Juan Antonio Ortega-Redondo ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Induction Motors ◽  
Spectral Component ◽  
Kinematic Chain ◽  
Theoretical Models ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Current Analysis ◽  
Multiple Faults ◽  
Bearing Defect ◽  
Component Identification

Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

scholarly journals Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
David Camarena-Martinez ◽  
Martin Valtierra-Rodriguez ◽  
Arturo Garcia-Perez ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Empirical Mode Decomposition ◽  
High Performance ◽  
Induction Motors ◽  
Industrial Applications ◽  
Feed Forward Neural Network ◽  
Linear Network ◽  
Multiple Faults ◽  
Online Systems ◽  
Time Operation ◽  
Mode Decomposition

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

scholarly journals Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1486
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque A. Osornio-Rios ◽  
Jose A. Antonino-Daviu ◽  
Jonathan Cureño-Osornio ◽  
Juan-Jose Saucedo-Dorantes
Keyword(s):  
Kinematic Chain ◽  
Industrial Applications ◽  
Electric Motors ◽  
Feed Forward Neural Network ◽  
Mechatronic Systems ◽  
Linear Discriminant ◽  
Bearing Faults ◽  
Outer Race ◽  
Non Invasive ◽  
Operation Conditions

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

scholarly journals Current perspective on production and applications of microbial cellulases: a review

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nisha Bhardwaj ◽  
Bikash Kumar ◽  
Komal Agrawal ◽  
Pradeep Verma
Keyword(s):  
Critical Factor ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Cellulolytic Enzymes ◽  
Promising Tool ◽  
The Status ◽  
Key Enzyme ◽  
Increasing Demand ◽  
Immobilization Techniques ◽  
Insight Into

AbstractThe potential of cellulolytic enzymes has been widely studied and explored for bioconversion processes and plays a key role in various industrial applications. Cellulase, a key enzyme for cellulose-rich waste feedstock-based biorefinery, has increasing demand in various industries, e.g., paper and pulp, juice clarification, etc. Also, there has been constant progress in developing new strategies to enhance its production, such as the application of waste feedstock as the substrate for the production of individual or enzyme cocktails, process parameters control, and genetic manipulations for enzyme production with enhanced yield, efficiency, and specificity. Further, an insight into immobilization techniques has also been presented for improved reusability of cellulase, a critical factor that controls the cost of the enzyme at an industrial scale. In addition, the review also gives an insight into the status of the significant application of cellulase in the industrial sector, with its techno-economic analysis for future applications. The present review gives a complete overview of current perspectives on the production of microbial cellulases as a promising tool to develop a sustainable and greener concept for industrial applications.

scholarly journals Experimental Study of Various Parameters during Speed Control of Three-phase Induction Motor Using GPIC and LabVIEW

2021 ◽  
Vol 5 (1) ◽  
pp. 51-62
Author(s):  
Adnan Ahmed ◽  
Abdul Majeed Shaikh ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Ahmed Shaikh ◽  
Jahangir Badar Soomro
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Induction Machine ◽  
Speed Control ◽  
Industrial Applications ◽  
General Purpose ◽  
Three Phase ◽  
Field Programmable ◽  
Input Source ◽  
Open Control

Induction motors are widely used from home to industrial applications. Speed of induction motor plays important role, so to control the speed of induction motor various techniques are adopted and one of these techniques is V/F control, which is adopted in this paper. This technique helps to control the speed in open control system in RPM. Moreover, Control is designed in LabVIEW, it is quite helpful to develop the circuit graphically and code is automatically written in the background to run on Field Programmable Gate Array (FPGA). The aim of this research is to study the impacts on diverse parameters during speed control of three phase induction machine with manipulation of GPIC. Solar technology is used as input source to drive the General-Purpose Inverter Controller (GPIC). Apart of this, impacts of modulation index and carrier frequency influencing the active, reactive and apparent power, temperature and power quality and current overshoot is analysed. MATLAB/Simulink and LabVIEW tools are used for simulation and results along with GPIC, Induction motor and solar panel as hardware.

scholarly journals High Accuracy Measurement of Total Losses in Fractional Horsepower Induction Motors

2015 ◽  
Vol 1 (2) ◽  
pp. 13
Author(s):  
Azzeddine Ferrah ◽  
Mounir Bouzguenda ◽  
Jehad M. Al-Khalaf Bani Younis
Keyword(s):  
Single Phase ◽  
Induction Motors ◽  
Data Acquisition System ◽  
High Accuracy ◽  
Industrial Applications ◽  
Motor Speed ◽  
Accuracy Measurement ◽  
Three Phase ◽  
Better Than ◽  
High Accuracy Measurement

Large and small single-phase and three-phase induction motors are commonly used in industrial applications. The present work represents an attempt towards the design of a high accuracy system for the measurement of fractional horsepower (FHP) induction motor losses and efficiency. The calorimeter designed and built is capable of measuring heat losses of up to 1 kW with an overall accuracy better than 3%. During all tests, ambient temperature, humidity, motor speed and motor frame temperature were recorded using precise digital instruments. The inlet, outlet temperatures and resulting losses were recorded automatically using a high accuracy 12-bit data acquisition system. The preliminary results obtained demonstrate the suitability of the designed calorimeter for the accurate measurement of losses in FHP induction motors.

scholarly journals Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1658 ◽  
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque Alfredo Osornio-Rios ◽  
Miguel Trejo-Hernandez ◽  
Rene de Jesus Romero-Troncoso ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Induction Motors ◽  
Early Stage ◽  
Industrial Sector ◽  
Smart Sensors ◽  
Discrete Wavelet ◽  
Electrical Machine ◽  
Smart Sensor ◽  
Time Frequency ◽  
Essential Components ◽  
Winding Insulation

Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.

scholarly journals Electrospinning of PCL-Based Blends: Processing Optimization for Their Scalable Production

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3853
Author(s):  
Marina P. Arrieta ◽  
Adrián Leonés Gil ◽  
Maysa Yusef ◽  
José M. Kenny ◽  
Laura Peponi
Keyword(s):  
Thermal Stability ◽  
Mechanical Response ◽  
Bulk Material ◽  
Processing Parameters ◽  
Industrial Sector ◽  
Industrial Applications ◽  
Elongation At Break ◽  
Processing Optimization ◽  
Scalable Production

In this work poly(ε-caprolactone) (PCL) based electrospun mats were prepared by blending PCL with microcrystalline cellulose (MCC) and poly(3-hydroxybutyrate) (PHB). The electrospinning processing parameters were firstly optimized with the aim to obtain scalable PCL-based electrospun mats to be used in the industrial sector. Neat PCL as well as PCL-MCC and PCL-PHB based mats in different proportions (99:1; 95:5; 90:10) were prepared. A complete morphological, thermal and mechanical characterization of the developed materials was carried out. Scanning electron microscopy (SEM) observations showed that the addition of PHB to the PCL matrix considerably reduced the formation of beads. Both the addition of MCC and PHB reduced the thermal stability of PCL, but obtained materials with enough thermal stability for the intended use. The electrospun PCL fibers show greatly reduced flexibility with respect to the PCL bulk material, however when PCL is blended with PHB their stretchability is increased, changing their elongation at break from 35% to 70% when 10 wt% of PHB is blended with PCL. However, the mechanical response of the different blends increases with respect to the neat electrospun PCL, offering the possibility to modulate their properties according to the required industrial applications.

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