Informative frequency band identification method using bi-frequency map clustering for fault detection in rotating machines

In presented work the problem of local damage detection in rolling element bearings is addressed. Usually such issues require the usage of the techniques of decomposition, separation etc. In such real industrial cases main difficulty lies in relatively low signal-to-noise ratio as well as unpredictable distribution of damage-related information in the frequency domain, hence the typical methods cannot be used. In this paper such industrial scenario is addressed and a simple yet effective approach to underlying component extraction will be discussed. Proposed method analyzes Cyclic Spectral Coherence map as starting data representation, and Expectation-Maximization is used as analytical tool to determine the informative frequency band (IFB) for impulsive component localization in the carrier frequency spectrum. Finally, based on identified IFB, the bandpass filter is constructed to extract the impulsive component from the input signal

The anomalous and smoothed anomalous envelope spectra for rotating machine fault diagnosis

The order-frequency spectral coherence and its integrated spectra (e.g. improved envelope spectrum, squared envelope spectrum) are some of the most powerful methods for performing fault diagnosis under time-varying operating conditions. However, it may require much work to interrogate the order-frequency spectral coherence for symptoms of damage. Hence, in this work we propose a methodology that combines the order-frequency spectral coherence with historical data that were acquired from a healthy machine to obtain an anomalous envelope spectrum, which is further processed for fault diagnosis. This anomalous envelope spectrum is further processed with a smoothing operation to not only perform automatic fault detection, but it is also possible to identify the damaged component if the kinematics of the gearbox are known. The proposed method is investigated on one numerical gearbox dataset and three experimental datasets, where its potential for performing automatic fault detection under time-varying operating conditions is highlighted.http://www.elsevier.com/locate/jnlabr/ymssphj2022Mechanical and Aeronautical Engineerin

Fuzzy determination of informative frequency band for bearing fault detection

Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults

KDamping: A Stiffness Based Vibration Absorption Concept

© 2016, © The Author(s) 2016. The KDamper is a novel passive vibration isolation and damping concept, based essentially on the optimal combination of appropriate stiffness elements, which include a negative stiffness element. The KDamper concept does not require any reduction in the overall structural stiffness, thus overcoming the corresponding inherent disadvantage of the “Quazi Zero Stiffness” (QZS) isolators, which require a drastic reduction of the structure load bearing capacity. Compared to the traditional Tuned Mass damper (TMD), the KDamper can achieve better isolation characteristics, without the need of additional heavy masses, as in the case of the T Tuned Mass damper. Contrary to the TMD and its variants, the KDamper substitutes the necessary high inertial forces of the added mass by the stiffness force of the negative stiffness element. Among others, this can provide comparative advantages in the very low frequency range. The paper proceeds to a systematic analytical approach for the optimal design and selection of the parameters of the KDamper, following exactly the classical approach used for the design of the Tuned Mass damper. It is thus theoretically proven that the KDamper can inherently offer far better isolation and damping properties than the Tuned Mass damper. Moreover, since the isolation and damping properties of the KDamper essentially result from the stiffness elements of the system, further technological advantages can emerge, in terms of weight, complexity and reliability. A simple vertical vibration isolation example is provided, implemented by a set of optimally combined conventional linear springs. The system is designed so that the system presents an adequate static load bearing capacity, whereas the Transfer Function of the system is below unity in the entire frequency range. Further insight is provided to the physical behavior of the system, indicating a proper phase difference between the positive and the negative stiffness elastic forces. This fact ensures that an adequate level of elastic forces exists throughout the entire frequency range, able to counteract the inertial and the external excitation forces, whereas the damping forces and the inertia forces of the additional mass remain minimal in the entire frequency range, including the natural frequencies. It should be mentioned that the approach presented does not simply refer to discrete vibration absorption device, but it consists a general vibration absorption concept, applicable also for the design of advanced materials or complex structures. Such a concept thus presents the potential for numerous implementations in a large variety of technological applications, whereas further potential may emerge in a multi-physics environment.status: publishe

A pre-processing methodology to enhance novel information for rotating machine diagnostics

Many sophisticated signal analysis techniques are developed to efficiently detect, localise and trend damage in rotating machine components such as bearings and gears for example. However, these techniques are generally applied without effectively incorporating historical information when performing condition monitoring. It is possible to enhance the performance of the analysis techniques by incorporating historical data from a machine in a reference condition. In this paper, a methodology is proposed to extract a novel signal i.e. a signal that contains information that is not present in the historical reference data, from a vibration signal. This is performed by utilising the available historical data. Sophisticated signal analysis techniques can subsequently be used on the novel vibration signal to diagnose the machine. The benefits of the methodology are illustrated on data, generated from phenomenological gearbox model data and experimental gearbox data, by utilising advanced techniques based on cyclostationary analysis. The results indicate that the novel vibration signal is more sensitive to damage, which highlights its potential as a pre-processing technique for rotating machine applications where historical data are available.K.C. Gryllias gratefully acknowledges the Research Fund KU Leuven.http://www.elsevier.com/locate/jnlabr/ymssp2020-06-01hj2019Mechanical and Aeronautical Engineerin

An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions

Please read abstract in the article.http://www.elsevier.com/locate/ymssphj2022Mechanical and Aeronautical Engineerin

Application of Intelligent Dynamics Signal Processing Methods for the Fault Diagnosis of Mechanical Systems

The demodulation of dynamic signals is studied using two approaches towards the aim of the automated export of the diagnostic information. The first approach is based on the Teager-Kaiser non-linear Differential Energy Operator and the Energy Operator Separation Algorithm – EOSA is used. The second approach is based on the Complex Shifted Morlet Wavelets. A number of methodologies and criteria have been proposed for the automatic selection of the two optimal wavelet parameters. These criteria are systematically analyzed and compared. A new criterion for the simultaneous optimal selection of the critical parameters is proposed, offering opportunities in order to automate the extraction of the envelope of the signal and detect most reliably and safest the demodulation band. Moreover, the detection of cracks in beam structures is attempted using a combination of the Principal Orthogonal Decomposition and the Morphological Processing. The four basic morphological operators are compared, using different types of structuring elements. The operator of erosion (erosion) with a curved (spline) structuring element presents the better results. The performance of the method is assessed using different beam structures that present a "breathing" crack and several factors, such as the boundary conditions, the location of the crack, the depth of the crack, the existence of multiple cracks, the distance between the multiple cracks, the level of noise in the measurements and the spatial resolution of the measured points. Furthermore, an automatic system for the fault diagnosis of ball element bearings is proposed, using the method of Support Vector Machines. The automatic fault diagnosis can be divided into two main parts, the input – preprocessing of the signals and the classification procedure. A hybrid algorithm which is based on a stochastic simulation model of the dynamic response of ball bearings, on the strategy of One-Against-All and on the method of Support Vector Machines is proposed. The simulated signals are generated by a well defined model that describes the dynamic response of damaged bearings. The features, which can be extracted from the spectra of both the raw signal and the envelope of the signal and characterize the changing condition of the engine, are considered. The key aspect is the extraction of the energy which is developed and spread at carefully, correctly and precisely defined frequency bands, that can be determined based on the good understanding of the nature of the phenomenon of the occurrence of the damage from an engineering standpoint. The successful definition of the frequency bands can lead to a more accurate, reliable and automated monitoring of rotating machinery and to accurate and on time fault diagnosis. Likewise in order to overcome the deterioration of the spectrum due to the emergence of transient phenomena, the use of Order Analysis is proposed. The proposed methods are evaluated using simulated and real signals. The real signals are derived from measurements performed at industrial plants and from international databases containing experimental measurements.Αρχικά μελετάται η αποδιαμόρφωση δυναμικών σημάτων με χρήση δύο προσεγγίσεων, με στόχο την αυτοματοποιημένη εξαγωγή διαγνωστικής πληροφορίας. Η πρώτη προσέγγιση βασίζεται στον Μη Γραμμικό Διαφορικό Ενεργειακό Τελεστή Teager-Kaiser και χρησιμοποιείται ο Αλγόριθμος Διαχωρισμού Ενεργειακού Τελεστή (Energy Operator Separation Algorithm - EOSA). Η δεύτερη προσέγγιση βασίζεται στη χρήση μετατοπισμένων μιγαδικών κυματιδίων Morlet. Για την αυτόματη και βέλτιστη επιλογή των δύο κυματιδιακών παραμέτρων έχουν προταθεί μια σειρά από μεθοδολογίες και κριτήρια. Τα κριτήρια αυτά ελέγχονται συστηματικά και συγκρίνονται μεταξύ τους. Προτείνεται ένα νέο κριτήριο για την ταυτόχρονη βέλτιστη επιλογή των κρισίμων παραμέτρων προσφέροντας δυνατότητες για αυτοματοποίηση της διαδικασίας υπολογισμού της περιβάλλουσας καθώς και εγκυρότερο και ασφαλέστερο εντοπισμό της ζώνης αποδιαμόρφωσης. Στη συνέχεια επιχειρείται ο εντοπισμός ρωγμών σε κατασκευές δοκών με συνδυασμό των μεθόδων Αποσύνθεσης Ορθοκανονικών Προβολών και Μορφολογικής Επεξεργασίας. Οι τέσσερεις βασικοί μορφολογικοί τελεστές συγκρίνονται, χρησιμοποιώντας διαφορετικούς τύπους δομικών στοιχείων. Ο τελεστής της διάβρωσης (erosion) με ένα καμπύλο (spline) δομικό στοιχείο φαίνεται να παρουσιάζει τα καλύτερα αποτελέσματα. Η απόδοση της μεθόδου εκτιμάται σε διαφορετικές κατασκευές δοκών που παρουσιάζουν μια “αναπνέουσα” ρωγμή, αναφορικά με διάφορους παράγοντες, όπως οι οριακές συνθήκες, η θέση της ρωγμής, το βάθος της ρωγμής, η ύπαρξη πολλαπλών ρωγμών, η απόσταση μεταξύ των πολλαπλών ρωγμών, το επίπεδο του θορύβου κατά την μέτρηση και η χωρική ευκρίνεια των μετρούμενων σημείων. Επιπλέον προτείνεται Αυτόματο Σύστημα Διάγνωσης Βλαβών Ένσφαιρων Τριβέων με χρήση της μεθόδου Μηχανών Διανυσματικής Υποστήριξης (Support Vector Machines). Η Αυτόματη Διάγνωση Βλαβών μπορεί να χωριστεί σε δύο βασικά τμήματα, στην εισαγωγή-προεπεξεργασία των σημάτων και στην μέθοδο ταξινόμησης. Προτείνεται ένας υβριδικός αλγόριθμος που βασίζεται σε ένα Στοχαστικό Μοντέλο Προσομοίωσης Δυναμικής Απόκρισης Ένσφαιρων Τριβέων, στην στρατηγική Ένας-Εναντίον-Όλων (One-Against-All) και στην χρησιμοποίηση της μεθόδου Μηχανών Διανυσματικής Υποστήριξης SVM. Τα προσομοιωμένα σήματα παράγονται από ένα καλά καθορισμένο μοντέλο που περιγράφει την δυναμική απόκριση φθαρμένων ένσφαιρων τριβέων. Εξετάζονται χαρακτηριστικά που μπορούν να εξαχθούν από τα φάσματα τόσο των αρχικών σημάτων όσο και της περιβάλλουσάς τους και αποτελούν περιγραφικούς δείκτες της μεταβολής της Κατάστασης Λειτουργίας της Μηχανής. Ως κύριο μέτρο-χαρακτηριστικό λαμβάνεται ο υπολογισμός της ενέργειας που κατανέμεται και αναπτύσσεται σε προσεκτικά, ορθά και αυστηρά καθορισμένες ζώνες συχνοτήτων, που μπορούν να καθοριστούν με βάση την καλή κατανόηση της φύσης του φαινομένου εμφάνισης της βλάβης από την σκοπιά ενός μηχανικού. Ο πετυχημένος ορισμός των συχνοτικών αυτών ζωνών μπορεί να οδηγήσει σε ακριβέστερη, πιο αξιόπιστη και αυτοματοποιημένη παρακολούθηση της κατάστασης λειτουργίας περιστρεφόμενου εξοπλισμού και στην έγκυρη και έγκαιρη διάγνωση βλαβών. Παράλληλα για να ξεπεραστεί η αλλοίωση του φάσματος συχνοτήτων λόγω εμφάνισης μεταβατικών φαινομένων προτείνεται η χρήση Ανάλυσης Τάξης (Order Analysis). Οι προτεινόμενες μέθοδοι αξιολογούνται με εφαρμογή τους σε προσομοιωμένα και πραγματικά σήματα. Τα πραγματικά σήματα προέρχονται από μετρήσεις που πραγματοποιήθηκαν σε βιομηχανικές εγκαταστάσεις και από διεθνείς βάσεις δεδομένων που περιλαμβάνουν μετρήσεις σε πειραματικές διατάξεις

Multi band integration on the cyclostationary bivariable methods for bearing diagnostics.

International audienceRolling element bearings are critical parts of rotating machinery, as they support the loads applied to the rotating components. Therefore, continuous monitoring of the health state of the operational bearings is applied in order to detect early damages before any unexpected breakdown of the rotating machinery occurs. Bearing diagnostics is a field of intensive research, focusing nowadays mainly in complicated machinery (e.g. planetary gearboxes, multi-stage gearboxes, etc.) operating under varying conditions (e.g. varying speed and load), as they still provide challenges in terms of accuracy and time of detection/diagnosis.One of the most common methods for bearings diagnostics is the Envelope Analysis. A filter is usually applied around an excited frequency band (by impulsive damage) and the signal is enveloped, thus obtaining the Squared Envelope Spectrum. For the detection of the filtering frequency band, several band selection tools have been proposed in the past that extract the optimal band in a semi-autonomous or fully autonomous manner. The most widely used tool for band selection is the Kurtogram, where the band that returns the highest Spectral Kurtosis value is selected as the optimal band for demodulation [1]. However, as the bearing damage may excite several frequency bands simultaneously, band-pass filtering around only one frequency band may not be sufficient for the detection of the bearing fault under the presence of noise. One proposed method to circumvent this case is to filter around several bands that carry the Signal of Interest (bearing damage signature). Recently, multi-band filtering based on the Autogram feature values, used as a pre-step in order to extract the Combined Squared Envelope Spectrum (CSES) has been presented, providing better detection performance of faulty bearings compared to the extraction of the SES after filtering over a single optimal band returned by the Autogram [2].Recently, a particular interest had been target to the Cyclic Spectral Correlation (CSC) and to the derived methods, due to their effectiveness in describing second-order cyclostationary signals. One of such methods is the Cyclic Spectral Coherence (CSCoh) which is a normalized version of the CSC bivariable map [3]. Both methods are represented in the frequency-frequency domain. It has been shown that the integration of the bivariable functions over discrete spectral frequency bands is analogous to band-pass filtering. The IESFOgram has been proposed [4] as a band selection tool, based on either the CSC or CSCoh, in order to extract the optimal frequency band. The integration on the frequency band of the bivariable map further enhances the detectability of faulty bearings on the resulting Improved Envelope Spectrum (IES). However, the method has been proposed with the integration of one single band. In this paper the method is extended towards the extraction of the Combined Improved Envelope Spectrum (CIES), performing a multi-band integration of the bivariable map around multiple resonant frequencies that are carriers of the bearing damage signature. The proposed method is applied, tested and evaluated on experimental data and the results are compared with other state-of-the-art band-selection tools

A Semi-supervised Support Vector Data Description-based Fault Detection Method for Rolling Element Bearings based on Cyclic Spectral Coherence

status: Published onlin