Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας, Θεόδωρος; Kostopoulos, V.

doi:10.5772/36166

Cited by 14 publications

(20 citation statements)

References 97 publications

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“…Another type of wavelet analysis called complex wavelet transform and is represented by the dualtree complex wavelet transform. It is an alternate, complex-valued extension and enhancement to the standard DWT, and has important properties that provides multiresolution, sparse representation and the capability to reduce the aliasing effects, which is caused by the overlap of opposing-frequency pass-bands of the wavelet filters [39], [40]. Two parallel DWTs with different low-pass and high-pass filters in each scale are used for decomposition and reconstruction in the dual-tree implementation.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

“…The N value also identifies the number of filter coefficients in each wavelet order; for instance, the wavelet dbN and symN have 2N coefficients in each order. Generally, the use of different wavelets to analyze the same signal would lead to different results, and to date no generic theoretical procedure has been published describing on how to select the optimum wavelet family [39], [45], [ 46].…”

Section: Selection the Optimum Mother Waveletmentioning

confidence: 99%

“…Some wavelet functions, such as db10 or sym7, have many coefficients in their filters, which will raise the execution time required for real-time wavelet analysis because of the increased computational burden on the embedded system. Also, higher order wavelet function will generate higher number of coefficients from the analysed signals that may case exceed the available system memory [39], [49]. The mother wavelet selection has been limited to the lower order families and hence there is no need to apply further quantitative methods as the remaining options are very few.…”

Section: Figure 10 Examples Of Mother Waveletsmentioning

confidence: 99%

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Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Jaber

Bicker

2018

IJECE

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Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Section: Selection the Optimum Mother Waveletmentioning

confidence: 99%

Section: Figure 10 Examples Of Mother Waveletsmentioning

confidence: 99%

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Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Jaber

Bicker

2018

IJECE

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“…Outline of the approach PHM approaches derive useful features directly from routinely monitored operational data. As with change of time wear in machinery increases, phenomena like excessive temperature, vibration or acoustic emission are observed [7], [14]. Prognostics approaches assume that the health indicators (features) are constant until a fault occurs in a component or system.…”

Section: Cumulative Features Extraction Procedures Based On Trigomentioning

confidence: 99%

A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling

Javed

Gouriveau

Zerhouni

et al. 2013

2013 IEEE Conference on Prognostics and Health Management (PHM)

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Performances of data-driven approaches are closely related to the form and trend of extracted features (that can be seen as time series health indicators). 1) Even if much of datadriven approaches are suitable to catch non-linearity in signals, features with monotonic trends (which is not always the case!) are likely to lead to better estimates. 2) Also, some classical extracted features do not show variation until a few time before failure occurs, which prevents performing RUL predictions in a timely manner to plan maintenance task. The aim of this paper is to present a novel feature extraction procedure to face with these two problems. Two aspects are considered. Firstly, the paper focuses on feature extraction in a new manner by utilizing trigonometric functions to extract features (health indicators) rather than typical statistic measures like RMS, etc. The proposed approach is applied on time-frequency analysis with Discrete Wavelet Transform (DWT). Secondly, a simple way of building new features based on cumulative functions is also proposed in order to transform time series into descriptors that depict accumulated wear. This approach can be extended to other types of features. The main idea of both developments is to map raw data with monotonic features with early trends, i.e., with descriptors that can be easily predicted. This methodology can enhance prognostics modeling and RUL prediction. The whole proposition is illustrated and discussed thanks to tests performed on vibration datasets from PRONOSTIA, an experimental platform that enables accelerated degradation of bearings.

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“…The particular work also covers general applications in machinery diagnostics in reference to wavelets. Loutas and Kostopoulos (2012) provide a more up to date list of existing applications of wavelets in CBM of gearboxes, bearings and electric motors.…”

Section: Fig 28mentioning

confidence: 99%

Methodologies for remaining useful life estimation with multiple sensors in rotating machinery

Roulias¹,

Roulias²

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Η παρούσα εργασία εστιάζεται στην ανάπτυξη μεθοδολογιών πρόβλεψης τελικής αστοχίας σε περιστρεφόμενα συστήματα με χρήση πολλαπλών αισθητήρων και μεθόδων μηχανικής μάθησης και επεξεργασίας σήματος. Το κίνητρο προήλθε από το κενό που υπάρχει στη βιβλιογραφία όσον αφορά την προγνωστική σε κιβώτια ταχυτήτων. Η προγνωστική σε έδρανα έχει μεν μελετηθεί αλλά σε μικρό βαθμό και η παρούσα εργασία έρχεται να συμβάλλει και σε αυτό τον τομέα.Στα πλαίσια αυτής της εργασίας εκπονήθηκε ένας αριθμός πειραμάτων κόπωσης κιβωτίων ταχυτήτων. Η μελέτη επεκτάθηκε πέραν της παρακολούθησης κατάστασης με τη μέθοδο των κραδασμών και συγκεκριμένα μελετήθηκαν καταγραφές σωματιδίων σιδήρου στο λιπαντικό (ODM) καθώς και Ακουστική Εκπομπής (AE). Η μέθοδος ΑΕ ευρέθη πιο στενά συσχετισμένη με τη σταδιακή υποβάθμιση της ακεραιότητας του κιβωτίου ταχυτήτων σε σχέση με τις καταγραφές κραδασμών. Επίσης με βάση τις καταγραφές του αισθητήρα σωματιδίων σιδήρου διακρίθηκαν δύο στάδια υποβάθμισης i) μια γραμμική περιοχή με σχεδόν σταθερό ρυθμό απελευθέρωσης υλικού από την επιφάνεια των δοντιών και ii) μια σύντομη αλλά έντονα μη γραμμική αύξηση στο ρυθμό αυτό πολύ κοντά στο τέλος της λειτουργίας του κιβωτίου.Tα πολύωρα πειράματα κόπωσης σε γρανάζια είναι πολύ απαιτητικά. Για να παρακαμφθεί αυτή η δυσκολία αναπτύχθηκε ένα φαινομενολογικό μοντέλο για αναπαραγωγή χρονοσειρών που ομοιάζουν σε καταγραφές γραναζιών σε κόπωση. Το μοντέλο αυτό στηρίχθηκε σε πραγματικά πειράματα κόπωσης. Έτσι έγινε δυνατό να εξεταστούν και να συγκριθούν ένας αριθμός μεθοδολογιών εκτίμησης εναπομένουσας ζωής και συγκεκριμένα i) Proportional Hazards Model (PHM), ii) ε- Support Vector Regression ε-SVR και iii) Exponential extrapolation βασισμένο σε μια διαδικασία bootstrap sampling.Στην παρούσα μελέτη προτείνεται ένα σύνολο παραμέτρων προερχόμενο από το πεδίο της συχνότητας, του χρόνου και κυματοπακέτων. Αυτό, συνδυαζόμενο με μια διαδικασία σύμπτυξης δεδομένων (ανάλυση σε πρωταρχικές και ανεξάρτητες συνιστώσες) αξιοποιείται για πρόγνωση σε γρανάζια σε κόπωση. Η τεχνική ανεξάρτητων συνιστωσών προτείνεται σαν προτιμότερη από τη σκοπιά της προγνωστικής καθώς βελτιώνει την εκτίμηση της εναπομένουσας ζωής.Η εργασία επεκτάθηκε και σε έδρανα κύλισης. Προτάθηκε μια διαδικασία wavelet denoising η οποία ενισχύει τόσο τη διαγνωστική όσο και την προγνωστική δυνατότητα του αισθητήρα κραδασμών. Τέλος, η σημασία της εξαγωγής παραμέτρων υπογραμμίζεται και στην περίπτωση της προγνωστικής σε έδρανα. Συνδυάζοντας πολλαπλές παραμέτρους και αισθητήρες κραδασμών μαζί με ένα μοντέλο ε-SVR παρέχεται ένα ολοκληρωμένο μοντέλο πιθανοτικής εκτίμησης εναπομένουσας ζωής σε έδρανα κύλισης ακόμα και σε περιπτώσεις που η τιμή RMS των κραδασμών δεν παρέχει πληροφορία.3.4 ΠΕΡΙΛΗΨΗ (στα Αγγλικά)The focus of this thesis was the development of failure prognosis methods (prognostics) in rotating machinery with use of multiple sensors digital signal processing and machine learning techniques. The motivation stems from the void in literature concerning prognostics in meshing gearboxes. Moreover, there are several but inconclusive works regarding bearing prognosis.Few research groups have studied multi-hour fatigue gear experiments and this was one of the contributions of this thesis. Moreover, the study expanded beyond the sheer application of vibration monitoring with the addition of an Oil Debris Monitoring probe (ODM) as well as Acoustic emission (AE).The method of AE monitoring is, once again, proposed as a robust technique for failure prognosis being better correlated with gear pitting level compared to the classic vibration monitoring technique. Moreover, judging from ODM recordings the gear pitting comprises of two phases i) a linear phase, with an almost constant pitting rate and ii) a very short non linear phase where the pitting rate increases exponentially, an explicit indication of a critical failure.Multi-hour gear experiments that are close to real scale applications are very demanding in time as well as in invested capital. To bypass this shortfall a gear failure like simulation is proposed. The simulation framework is based on real life experiments and is applied to assess a number of data-driven Remaining Useful Life (RUL) estimation techniques namely i) Proportional Hazards Μodel (PHM), ii) ε- Support Vector Regression ε-SVR and iii) Exponential extrapolation based on bootstrap sampling.In the current thesis a feature extraction scheme for prognosis is proposed and assessed based on time domain, frequency domain statistical features and Wavelet Packet (WP) energy derived from AE and vibration recordings. ICA is proposed as a preferable fusion technique for gear failure prognostics. Application of ICA for feature fusion provided a clear improvement regarding the earlier presented bootstrap extrapolation technique.Bearings are also taken into account since they are closely connected to gearboxes. In the current thesis a wavelet denoising method is proposed for bearing vibration recordings aiming to the improvement of the diagnostic and prognostic potential of vibration. Finally the importance of data fusion is highlighted in the case of bearings. It is observed that a feature extraction scheme can generalize the application of prognostics, even in cases where RMS may yield no important degradation trend.

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Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Cited by 14 publications

References 97 publications

Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling

Methodologies for remaining useful life estimation with multiple sensors in rotating machinery

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