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Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.
Juan-Jose Saucedo-Dorantes; Israel Zamudio-Ramirez; Jonathan Cureno-Osornio; Roque Alfredo Osornio-Rios; Jose Alfonso Antonino-Daviu. Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network. Applied Sciences 2021, 11, 8033 .
AMA StyleJuan-Jose Saucedo-Dorantes, Israel Zamudio-Ramirez, Jonathan Cureno-Osornio, Roque Alfredo Osornio-Rios, Jose Alfonso Antonino-Daviu. Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network. Applied Sciences. 2021; 11 (17):8033.
Chicago/Turabian StyleJuan-Jose Saucedo-Dorantes; Israel Zamudio-Ramirez; Jonathan Cureno-Osornio; Roque Alfredo Osornio-Rios; Jose Alfonso Antonino-Daviu. 2021. "Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network." Applied Sciences 11, no. 17: 8033.
Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machines integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of Bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the Bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.
Miguel E. Iglesias-Martinez; Jose Antonino-Daviu; Pedro Fernandez de Cordoba; J Alberto Conejero; Larisa Dunai. Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals. IEEE Transactions on Industry Applications 2021, PP, 1 -1.
AMA StyleMiguel E. Iglesias-Martinez, Jose Antonino-Daviu, Pedro Fernandez de Cordoba, J Alberto Conejero, Larisa Dunai. Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals. IEEE Transactions on Industry Applications. 2021; PP (99):1-1.
Chicago/Turabian StyleMiguel E. Iglesias-Martinez; Jose Antonino-Daviu; Pedro Fernandez de Cordoba; J Alberto Conejero; Larisa Dunai. 2021. "Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals." IEEE Transactions on Industry Applications PP, no. 99: 1-1.
The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.
Vincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps. Energies 2021, 14, 4514 .
AMA StyleVincent Becker, Thilo Schwamm, Sven Urschel, Jose Antonino-Daviu. Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps. Energies. 2021; 14 (15):4514.
Chicago/Turabian StyleVincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. 2021. "Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps." Energies 14, no. 15: 4514.
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
Israel Zamudio-Ramirez; Roque Osornio-Rios; Jose Antonino-Daviu; Jonathan Cureño-Osornio; Juan-Jose Saucedo-Dorantes. Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals. Electronics 2021, 10, 1486 .
AMA StyleIsrael Zamudio-Ramirez, Roque Osornio-Rios, Jose Antonino-Daviu, Jonathan Cureño-Osornio, Juan-Jose Saucedo-Dorantes. Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals. Electronics. 2021; 10 (12):1486.
Chicago/Turabian StyleIsrael Zamudio-Ramirez; Roque Osornio-Rios; Jose Antonino-Daviu; Jonathan Cureño-Osornio; Juan-Jose Saucedo-Dorantes. 2021. "Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals." Electronics 10, no. 12: 1486.
This paper proposes the qualitative and quantitative analysis of stray-flux and current data under starting to detect damper faults in cylindrical rotor synchronous machines. These machines are typically employed in high power applications and their possible outages may imply huge costs for the industries or plants where they operate. The damper cage is a critical part of these machines and a potential source of catastrophic failures. However, few research works have provided feasible alternatives to monitor the condition of such element. This work analyses the viability of analyzing the electromotive force signals induced by the stray-flux in external coil sensors as well as current signals under starting to diagnose damper faults. The results obtained with laboratory machines with different levels of damper damage show that the analyses of those signals can provide very useful information for determining how the damper degrades over time. Moreover, the paper studies the effect of the remanent magnetism over the viability of the approaches and provides solutions to overcome this problem. The conclusions are valuable for field engineers since, nowadays, there are few available solutions that allow monitoring the condition of such element without motor disassembly.
Habib Castro-Coronado; Jose Antonino-Daviu; Alfredo Quijano-Lopez; Pedro Llovera-Segovia; Vicente Fuster-Roig; Luis Serrano-Iribarnegaray; Larisa Dunai. Evaluation of the Damper Condition in Synchronous Motors Through the Analysis of the Transient Stray Fluxes and Currents Considering the Effect of the Remanent Magnetism. IEEE Transactions on Industry Applications 2021, 57, 4665 -4674.
AMA StyleHabib Castro-Coronado, Jose Antonino-Daviu, Alfredo Quijano-Lopez, Pedro Llovera-Segovia, Vicente Fuster-Roig, Luis Serrano-Iribarnegaray, Larisa Dunai. Evaluation of the Damper Condition in Synchronous Motors Through the Analysis of the Transient Stray Fluxes and Currents Considering the Effect of the Remanent Magnetism. IEEE Transactions on Industry Applications. 2021; 57 (5):4665-4674.
Chicago/Turabian StyleHabib Castro-Coronado; Jose Antonino-Daviu; Alfredo Quijano-Lopez; Pedro Llovera-Segovia; Vicente Fuster-Roig; Luis Serrano-Iribarnegaray; Larisa Dunai. 2021. "Evaluation of the Damper Condition in Synchronous Motors Through the Analysis of the Transient Stray Fluxes and Currents Considering the Effect of the Remanent Magnetism." IEEE Transactions on Industry Applications 57, no. 5: 4665-4674.
The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.
Oscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors 2021, 21, 3910 .
AMA StyleOscar Pardo-Zamora, Rene Romero-Troncoso, Jesus Millan-Almaraz, Daniel Morinigo-Sotelo, Roque Osornio-Rios, Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors. 2021; 21 (11):3910.
Chicago/Turabian StyleOscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. 2021. "Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization." Sensors 21, no. 11: 3910.
Motor current signature analysis (MCSA) for fault detection has found widespread application, especially for induction motors (IM). The basis of MCSA is the evaluation of a motor?s current. This analysis is now also used for other motor types and can be used to detect faults of the coupled load. The purpose of this paper is to examine whether MCSA can be used to detect faults in a wet-rotor pump. A total of three faults are examined. The results show that, compared to a healthy pump, all faults could be detected. However, a detailed analysis of frequency components has to be carried out to differentiate the faults. A circulation pump with a maximum power consumption of 1.1 kW was used as the test item.
Vincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation. IEEE Transactions on Industry Applications 2021, 57, 4617 -4624.
AMA StyleVincent Becker, Thilo Schwamm, Sven Urschel, Jose Antonino-Daviu. Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation. IEEE Transactions on Industry Applications. 2021; 57 (5):4617-4624.
Chicago/Turabian StyleVincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. 2021. "Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation." IEEE Transactions on Industry Applications 57, no. 5: 4617-4624.
Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical motors is an important task, because it allows saving a large amount of money and time. An analysis of acoustic signals is a promising tool to improve the accuracy of fault diagnosis. It is essential to analyze acoustic signals to assess the state of the motor. In this paper, three electric impact drills (EID) were analyzed using acoustic signals: healthy EID, EID with damaged rear bearing, EID with damaged front bearing. Three angle grinders (AG) were analyzed: healthy AG, AG with 1 blocked air inlet, AG with 2 blocked air inlets. The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components). Acoustic features vectors were classified by the nearest neighbor classifier and Naive Bayes classifier. The classification accuracy were in the range of 89.33–97.33% for three electric impact drills. The classification accuracy were in the range of 90.66–100% for three angle grinders. The presented method is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools. It can be also useful for diagnosis of similar power tools.
Adam Glowacz; Ryszard Tadeusiewicz; Stanislaw Legutko; Wahyu Caesarendra; Muhammad Irfan; Hui Liu; Frantisek Brumercik; Miroslav Gutten; Maciej Sulowicz; Jose Alfonso Antonino Daviu; Thompson Sarkodie-Gyan; Pawel Fracz; Anil Kumar; Jiawei Xiang. Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Applied Acoustics 2021, 179, 108070 .
AMA StyleAdam Glowacz, Ryszard Tadeusiewicz, Stanislaw Legutko, Wahyu Caesarendra, Muhammad Irfan, Hui Liu, Frantisek Brumercik, Miroslav Gutten, Maciej Sulowicz, Jose Alfonso Antonino Daviu, Thompson Sarkodie-Gyan, Pawel Fracz, Anil Kumar, Jiawei Xiang. Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Applied Acoustics. 2021; 179 ():108070.
Chicago/Turabian StyleAdam Glowacz; Ryszard Tadeusiewicz; Stanislaw Legutko; Wahyu Caesarendra; Muhammad Irfan; Hui Liu; Frantisek Brumercik; Miroslav Gutten; Maciej Sulowicz; Jose Alfonso Antonino Daviu; Thompson Sarkodie-Gyan; Pawel Fracz; Anil Kumar; Jiawei Xiang. 2021. "Fault diagnosis of angle grinders and electric impact drills using acoustic signals." Applied Acoustics 179, no. : 108070.
Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.
Rashid Naseem; Zain Shaukat; Muhammad Irfan; Muhammad Arif Shah; Arshad Ahmad; Fazal Muhammad; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu; Adel Sulaiman. Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics 2021, 10, 168 .
AMA StyleRashid Naseem, Zain Shaukat, Muhammad Irfan, Muhammad Arif Shah, Arshad Ahmad, Fazal Muhammad, Adam Glowacz, Larisa Dunai, Jose Antonino-Daviu, Adel Sulaiman. Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics. 2021; 10 (2):168.
Chicago/Turabian StyleRashid Naseem; Zain Shaukat; Muhammad Irfan; Muhammad Arif Shah; Arshad Ahmad; Fazal Muhammad; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu; Adel Sulaiman. 2021. "Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction." Electronics 10, no. 2: 168.
The limitations of the thermal, vibration, or electrical monitoring of electric machines such as false indications, low sensitivity, and difficulty of fault interpretation have recently been exposed. This has led to a shift in the direction in research toward applying new techniques for improving the reliability of condition monitoring. With the changing environment, the purpose of this article is to provide an overview of the recent trends in the industrial demand and research activity in condition monitoring technology. The new developments in insulation testing, electrical testing, flux analysis, transient analysis, and fault prognostics are summarized. The challenges and recommendations for future work for the new technologies are also explored to help support target research and development efforts according to industrial needs.
Sang Bin Lee; Greg C. Stone; Jose Antonino-Daviu; Konstantinos N. Gyftakis; Elias G. Strangas; Pascal Maussion; Carlos A. Platero. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. IEEE Industrial Electronics Magazine 2020, 14, 158 -167.
AMA StyleSang Bin Lee, Greg C. Stone, Jose Antonino-Daviu, Konstantinos N. Gyftakis, Elias G. Strangas, Pascal Maussion, Carlos A. Platero. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. IEEE Industrial Electronics Magazine. 2020; 14 (4):158-167.
Chicago/Turabian StyleSang Bin Lee; Greg C. Stone; Jose Antonino-Daviu; Konstantinos N. Gyftakis; Elias G. Strangas; Pascal Maussion; Carlos A. Platero. 2020. "Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges." IEEE Industrial Electronics Magazine 14, no. 4: 158-167.
The detection of rotor failures in synchronous motors is a matter of primordial interest in many industrial sites where these machines are critical assets. However, due to the particular operation of these motors, most conventional techniques relying on steady-state analysis, commonly used in other electric machines, are not applicable to such motors. In this context, it has been recently proven that the analysis of different quantities under transient operation of the motor and, more specifically, under motor starting can provide crucial information for the diagnosis of many faults. This work proposes the time-frequency analysis of stray fluxes and currents to detect field winding faults in synchronous motors. The potential consequences of this fault can be catastrophic for the motor integrity, so that the detection of its presence in its early stages can be of critical importance for the industry. The results included in this paper prove the usefulness of the transient analysis of such non-invasive quantities not only to detect the presence of the field winding fault but also to set a starting point to determine its severity.
Pengfei Tian; Jose Alfonso Antonino-Daviu; Carlos A. Platero; Larisa Dunai Dunai. Detection of Field Winding Faults in Synchronous Motors via Analysis of Transient Stray Fluxes and Currents. IEEE Transactions on Energy Conversion 2020, 36, 2330 -2338.
AMA StylePengfei Tian, Jose Alfonso Antonino-Daviu, Carlos A. Platero, Larisa Dunai Dunai. Detection of Field Winding Faults in Synchronous Motors via Analysis of Transient Stray Fluxes and Currents. IEEE Transactions on Energy Conversion. 2020; 36 (3):2330-2338.
Chicago/Turabian StylePengfei Tian; Jose Alfonso Antonino-Daviu; Carlos A. Platero; Larisa Dunai Dunai. 2020. "Detection of Field Winding Faults in Synchronous Motors via Analysis of Transient Stray Fluxes and Currents." IEEE Transactions on Energy Conversion 36, no. 3: 2330-2338.
Carbon nanotubes (CNTs) and graphene are extensively studied materials in the field of sensing technology and other electronic devices due to their better functional and structural properties. Additionally, more attention is given to utilize these materials as a filler to reinforce the properties of other materials. However, the role of weight percentage of CNTs in the piezoresistive properties of these materials has not been reported yet. In this work, CNT-graphene composite-based piezoresistive pressure samples in the form of pellets with different weight percentages of CNTs were fabricated and characterized. All the samples exhibit a decrease in the direct current (DC) resistance with the increase in external uniaxial applied pressure from 0 to 74.8 kNm−2. However, under the same external uniaxial applied pressure, the DC resistance exhibit more decrease as the weight percentage of the CNTs increase in the composites.
Asar Ali; Farman Ali; Muhammad Irfan; Fazal Muhammad; Adam Glowacz; Jose Alfonso Antonino-Daviu; Wahyu Caesarendra; Salman Qamar. Mechanical Pressure Characterization of CNT-Graphene Composite Material. Micromachines 2020, 11, 1000 .
AMA StyleAsar Ali, Farman Ali, Muhammad Irfan, Fazal Muhammad, Adam Glowacz, Jose Alfonso Antonino-Daviu, Wahyu Caesarendra, Salman Qamar. Mechanical Pressure Characterization of CNT-Graphene Composite Material. Micromachines. 2020; 11 (11):1000.
Chicago/Turabian StyleAsar Ali; Farman Ali; Muhammad Irfan; Fazal Muhammad; Adam Glowacz; Jose Alfonso Antonino-Daviu; Wahyu Caesarendra; Salman Qamar. 2020. "Mechanical Pressure Characterization of CNT-Graphene Composite Material." Micromachines 11, no. 11: 1000.
The router plays an important role in communication among different processing cores in on-chip networks. Technology scaling on one hand has enabled the designers to integrate multiple processing components on a single chip; on the other hand, it becomes the reason for faults. A generic router consists of the buffers and pipeline stages. A single fault may result in an undesirable situation of degraded performance or a whole chip may stop working. Therefore, it is necessary to provide permanent fault tolerance to all the components of the router. In this paper, we propose a mechanism that can tolerate permanent faults that occur in the router. We exploit the fault-tolerant techniques of resource sharing and paring between components for the input port unit and routing computation (RC) unit, the resource borrowing for virtual channel allocator (VA) and multiple paths for switch allocator (SA) and crossbar (XB). The experimental results and analysis show that the proposed mechanism enhances the reliability of the router architecture towards permanent faults at the cost of 29% area overhead. The proposed router architecture achieves the highest Silicon Protection Factor (SPF) metric, which is 24.8 as compared to the state-of-the-art fault-tolerant architectures. It incurs an increase in latency for SPLASH2 and PARSEC benchmark traffics, which is minimal as compared to the baseline router.
Ayaz Hussain; Muhammad Irfan; Naveed Khan Baloch; Umar Draz; Tariq Ali; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu. Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip. Electronics 2020, 9, 1783 .
AMA StyleAyaz Hussain, Muhammad Irfan, Naveed Khan Baloch, Umar Draz, Tariq Ali, Adam Glowacz, Larisa Dunai, Jose Antonino-Daviu. Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip. Electronics. 2020; 9 (11):1783.
Chicago/Turabian StyleAyaz Hussain; Muhammad Irfan; Naveed Khan Baloch; Umar Draz; Tariq Ali; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu. 2020. "Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip." Electronics 9, no. 11: 1783.
Pumps have a wide range of applications. Methods for fault detection of motors are increasingly being used for pumps. In the context of this paper, a test bench is built to investigate circulation pumps for faults. As a use case, the fault of impeller clogging was first measured and then examined with the help of motor current signature analysis. It can be seen that there are four frequencies at which there is an increase in amplitude in case of a fault. The sidebands around the supply frequency are in particular focus. The clogging of three and four of a total of seven channels leads to the highest amplitudes at the fault frequencies. The efficiency is reduced by 9 to 15% in case of faulty operation. These results indicate that the implementation of fault detection algorithms on the pump electronics represents added value for the pump operator. Furthermore, the results can be transferred to other applications.
Vincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. Fault Investigation of Circulation Pumps to Detect Impeller Clogging. Applied Sciences 2020, 10, 7550 .
AMA StyleVincent Becker, Thilo Schwamm, Sven Urschel, Jose Antonino-Daviu. Fault Investigation of Circulation Pumps to Detect Impeller Clogging. Applied Sciences. 2020; 10 (21):7550.
Chicago/Turabian StyleVincent Becker; Thilo Schwamm; Sven Urschel; Jose Antonino-Daviu. 2020. "Fault Investigation of Circulation Pumps to Detect Impeller Clogging." Applied Sciences 10, no. 21: 7550.
We apply power spectral analysis based on covariance function and spectral subtraction to detect adjacent and non-adjacent bar breakages. We obtain a spectral pattern when the signal presents one or various broken bars, independent of the relative position of the bar breakages. The proposed algorithm gives satisfactory results for detectability compared to some previous research. Additionally, we also present illustrations of faults and signal to noise in the noise-reduction stage.
Miguel Enrique Iglesias Martínez; Pedro Fernández De Córdoba; Jose Alfonso Antonino-Daviu; J. Alberto Conejero. Detection of Adjacent and Non-Adjacent Bar Breakages in Induction Motors Based on Power Spectral Subtraction and Second Order Statistics of Sound Signals. Applied Sciences 2020, 10, 6641 .
AMA StyleMiguel Enrique Iglesias Martínez, Pedro Fernández De Córdoba, Jose Alfonso Antonino-Daviu, J. Alberto Conejero. Detection of Adjacent and Non-Adjacent Bar Breakages in Induction Motors Based on Power Spectral Subtraction and Second Order Statistics of Sound Signals. Applied Sciences. 2020; 10 (19):6641.
Chicago/Turabian StyleMiguel Enrique Iglesias Martínez; Pedro Fernández De Córdoba; Jose Alfonso Antonino-Daviu; J. Alberto Conejero. 2020. "Detection of Adjacent and Non-Adjacent Bar Breakages in Induction Motors Based on Power Spectral Subtraction and Second Order Statistics of Sound Signals." Applied Sciences 10, no. 19: 6641.
Tool condition monitoring (TCM) is one of the most relevant tasks during a machining process. Latest high-quality productivity standards make it essential to monitor the cutting tool wearing. Current TCM methodologies demand the installation of sensors near the working area, which in practical terms, it is not the most optimal solution since the final diagnosis can be disturbed by noisy signals and direct interferences with the machining process. This paper proposes a novel non-invasive methodology based on the time-frequency analysis of the stray flux captured around the spindle-motor to detect and estimate the wearing level in cutting tools. Moreover, a new fault indicator based on this quantity is introduced through the application of the discrete wavelet transform (DWT). The results obtained are promising and demonstrates the effectiveness of the proposal to become a complementary source of information to classical approaches. This is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wearing levels and different cutting depths
Israel Zamudio-Ramirez; Jose Alfonso Antonino-Daviu; Miguel Trejo-Hernandez; Roque A. Alfredo Osornio-Rios. Cutting Tool Wear Monitoring in CNC Machines Based in Spindle-Motor Stray Flux Signals. IEEE Transactions on Industrial Informatics 2020, PP, 1 -1.
AMA StyleIsrael Zamudio-Ramirez, Jose Alfonso Antonino-Daviu, Miguel Trejo-Hernandez, Roque A. Alfredo Osornio-Rios. Cutting Tool Wear Monitoring in CNC Machines Based in Spindle-Motor Stray Flux Signals. IEEE Transactions on Industrial Informatics. 2020; PP (99):1-1.
Chicago/Turabian StyleIsrael Zamudio-Ramirez; Jose Alfonso Antonino-Daviu; Miguel Trejo-Hernandez; Roque A. Alfredo Osornio-Rios. 2020. "Cutting Tool Wear Monitoring in CNC Machines Based in Spindle-Motor Stray Flux Signals." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.
Electric motors condition monitoring is a field of paramount importance for industry. In recent decades, there has been a continuous effort to investigate new techniques and methods that are able to determine the health of these machines with high accuracy and reliability. Classical methods based on the analysis of diverse machine quantities under stationary conditions are being replaced by modern methodologies that are adapted to any operation regime of the machine (including transients). These new methods (especially those based on motor startup signal monitoring), which imply the use of advanced signal processing tools, have shown great potential and have provided spectacular advantages versus conventional approaches enabling, among other facts, a much more reliable determination of the machine health. This paper reviews the background of this recent condition monitoring trend and shows the advantages of this new approach with regard to its application to the analysis of electrical quantities. Examples referred to its application to real motors operating in industry are included, proving the huge potential of the transient-based approach and its benefits versus conventional methods.
Jose Antonino-Daviu. Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance. Applied Sciences 2020, 10, 6137 .
AMA StyleJose Antonino-Daviu. Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance. Applied Sciences. 2020; 10 (17):6137.
Chicago/Turabian StyleJose Antonino-Daviu. 2020. "Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance." Applied Sciences 10, no. 17: 6137.
Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.
Ayaz Hussain; Umar Draz; Tariq Ali; Saman Tariq; Muhammad Irfan; Adam Glowacz; Jose Alfonso Antonino Daviu; Sana Yasin; Saifur Rahman. Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies 2020, 13, 3930 .
AMA StyleAyaz Hussain, Umar Draz, Tariq Ali, Saman Tariq, Muhammad Irfan, Adam Glowacz, Jose Alfonso Antonino Daviu, Sana Yasin, Saifur Rahman. Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 2020; 13 (15):3930.
Chicago/Turabian StyleAyaz Hussain; Umar Draz; Tariq Ali; Saman Tariq; Muhammad Irfan; Adam Glowacz; Jose Alfonso Antonino Daviu; Sana Yasin; Saifur Rahman. 2020. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach." Energies 13, no. 15: 3930.
Rotating machines have been used in a wide variety of industries, such as manufacturing tools
Grzegorz Królczyk; Zhixiong Li; Jose Alfonso Antonino Daviu. Fault Diagnosis of Rotating Machine. Applied Sciences 2020, 10, 1961 .
AMA StyleGrzegorz Królczyk, Zhixiong Li, Jose Alfonso Antonino Daviu. Fault Diagnosis of Rotating Machine. Applied Sciences. 2020; 10 (6):1961.
Chicago/Turabian StyleGrzegorz Królczyk; Zhixiong Li; Jose Alfonso Antonino Daviu. 2020. "Fault Diagnosis of Rotating Machine." Applied Sciences 10, no. 6: 1961.
Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.
Israel Zamudio Ramirez; Roque Alfredo Osornio-Ríos; Jose Alfonso Antonino-Daviu; Alfredo Quijano-Lopez. Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis. Sensors 2020, 20, 1477 .
AMA StyleIsrael Zamudio Ramirez, Roque Alfredo Osornio-Ríos, Jose Alfonso Antonino-Daviu, Alfredo Quijano-Lopez. Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis. Sensors. 2020; 20 (5):1477.
Chicago/Turabian StyleIsrael Zamudio Ramirez; Roque Alfredo Osornio-Ríos; Jose Alfonso Antonino-Daviu; Alfredo Quijano-Lopez. 2020. "Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis." Sensors 20, no. 5: 1477.