Chromatic monitoring of gear mechanical degradation based on acoustic emission

This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.Peer ReviewedPostprint (published version

Material defect reconstruction by non-destructive testing with laser induced ultrasonics

Aligned with current research efforts and industrial applications on nondestructive testing, in this work, a hybrid system combining remotely induced laser ultrasonics with conventional transducer detection is studied for defects detection in metallic parts. The processing of the induced acoustic emission waves is proposed to be approached by means of a high-resolution volumetric signal processing procedure based on the synthetic aperture focusing technique for the benefit of the final 2D visualization of the defects. The advantages of the hybrid non-destructive testing approach and the performance of the processing technique are experimentally validated.Peer ReviewedPostprint (published version

Detection of partial demagnetization fault in PMSMs operating under nonstationary conditions

Demagnetization fault detection of in-service permanent magnet synchronous machines (PMSMs) is a challenging task, because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold-Kalman filter is proposed to detect the partial demagnetization fault in PMSMs running at nonstationary conditions. The amplitude of envelope of the fault characteristic orders is used as fault indictor. Experimental results verify the superiority of the proposed method on the partial demagnetization online fault detection of PMSMs under various speed and load conditions.Postprint (author's final draft

Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Performance analysis of acoustic emission hit detection methods using time features

Acoustic emission (AE) analysis is a powerful potential characterisation method forfracture mechanism analysis during metallic specimen testing. Nevertheless, identifying and extracting each event when analysing the raw signal remains a major challenge. Typically, AEdetection is carried out using a thresholding approach. However, thoughextensively applied, this approach presents some critical limitationsdue to overlapping transients,differences in strength and low signal-to-noise ratio.To address these limitations, advancedmethodologies for detecting AE hits have been developedin the literature. The most prominently used are instantaneous amplitude, the short-termaverage to long-term average ratio,the Akaike information criterionandwaveletanalysis, each of which exhibits satisfactory performance and easeof implementationfordiverseapplications. However, their pronenessto errors in the presence of non-cyclostationary AEwavefrontsand the lack of thoroughcomparison for transient AE signalsare constraints to the wider application of these methodsin non-destructive testing procedures.In this studywith the aim of make aware about the drawbacks of the traditionalthreshold approach, a comprehensiveanalysis ofits limiting factorswhentaking in regard the AE waveformbehaviouris presented.Additionallyin a second section, a performance analysis of the main advanced representative-methods in the field is carried out throughacommon comparative framework, by analysing first, AE waves generated from a standardisedHsu-Nielsen testand second, adata frame of a highly active signal derivedfrom a tensile test.With the aim to quantify the performance with which theseAE detection methodologies work, for the first time in literature, time features as the endpoint and duration accuracies, as well as statistical metricsas accuracy, precisionand false detection rates, are studied.Postprint (author's final draft

Bearing fault diagnosis by EXIN CCA

EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninvariant CCA projection and allows representing data drawn under different operating conditions. It can be applied to data visualization, interpretation (as a kind of sensor of the underlying physical phenomenon) and classification for real time industrial applications. Here an example is given for bearing fault diagnostics in an electromechanical device.Peer ReviewedPostprint (published version

Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain

© 2016 Juan Jose Saucedo-Dorantes et al. 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.Postprint (published version

Activity-aware HVAC power demand forecasting

The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to improve the dynamics responsiveness and context awareness of the demand prediction system, thus improving its accuracy by taking into account the usage pattern of the building. A dedicated activity prediction model supported by a recurrent neural network is built considering this specific indicator, which is then integrated with a power demand model built with an adaptive neuro-fuzzy inference system. Since the power demand is not directly available, an estimation method is proposed, which permits the indirect monitoring of the aggregated power consumption of the terminal units. The presented methodology is validated experimentally in terms of accuracy and performance using real data from a research building, showing that the accuracy of the power prediction can be improved when using a specialized modeling structure to estimate the building's activity.Peer ReviewedPostprint (author's final draft