Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions

Condition monitoring of a fan using neural networks

Master of Science (M.Sc.) in Computational Science

Improving the profitability, availability and condition monitoring of FPSO terminals

The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles

Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...

Acoustic emission monitoring of propulsion systems : a laboratory study on a small gas turbine

The motivation of the work is to investigate a new, non-intrusive condition monitoring system for gas turbines with capabilities for earlier identification of any changes and the possibility of locating the source of the faults. This thesis documents experimental research conducted on a laboratory-scale gas turbine to assess the monitoring capabilities of Acoustic Emission (AE). In particular it focuses on understanding the AE behaviour of gas turbines under various normal and faulty running conditions. A series of tests was performed with the turbine running normally, either idling or with load. Two abnormal running configurations were also instrumented in which the impeller was either prevented from rotation or removed entirely. With the help of demodulated resonance analysis and an ANN it was possible to identify two types of AE; a background broadband source which is associated with gas flow and flow resistance, and a set of spectral frequency peaks which are associated with reverberation in the exhaust and coupling between the alternator and the turbine. A second series of experiments was carried out with an impeller which had been damaged by removal of the tips of some of the blades (two damaged blades and four damaged blades). The results show the potential capability of AE to identify gas turbine blade faults. The AE records showed two obvious indicators of blade faults, the first being that the energy in the AE signals becomes much higher and is distinctly periodic at higher speeds, and the second being the appearance of particular pulse patterns which can be characterized in the demodulated frequency domain

Optimization of indentification of particle impacts using acoustic emission.

Air-borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles transported at severe operating conditions such as high-flow velocity can cause concerns for structural integrity, through wear originating from particle impacts with the structure. To apply Acoustic Emission (AE) in particle-impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy - calculated from the recorded free-falling or air-borne particle impact AE signals - was correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type is present in the system (particularly with similar size, density and impact velocity), calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and operating conditions either did not account for any bubble formation in the system or, if they did, did so at least an order of magnitude lower in amplitude than the sand particle impact. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries such as sand production during oil and gas transportation away from a reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand-free flow conditions. In real-time monitoring for a specific calibrated flow, the flow-generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which it often does in the process industry, the calibration is no longer valid and AE events from bubbles can be misinterpreted as sand particle impacts, and vice versa. In this thesis, sand particles and glass beads (with similar size, density and impact velocity) have been studied, dropping from 200 mm using a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off-centred, on the opposite side of the impacting surface. Signal analysis has been carried out by evaluating seven standard AE parameters (amplitude, energy, rise time, duration, power spectral density (PSD), peak frequency at PSD, and spectral centroid) in the time and frequency domain, and time-frequency domain analysis has been performed by applying Gabor Wavelet Transformation. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency-band energy ratio - has been proposed. This technique is able to distinguish between the populations of two very similar (in terms of size, mass and energy) groups of sand particles and glass beads impacting on mild steel, based on the coefficient of variation (Cv) of the frequency-band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using a Support Vector Machine (SVM)-based classification algorithm with seven standard AE parameters, evaluated in both the time and frequency domain. The available dataset has been segmented into two parts: training set (80%) and test set (20%). The developed model has been applied on the test data for the purpose of model performance evaluation. The overall success rate in individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86%, and at S2 as 92%. To study wet particle impacts on a wet target surface in the presence of bubbles, the target plate was sealed to a cylindrical perspex tube. Single and multiple sand particles were introduced in the system using a constant-speed blower, to impact the target surface under water-loading. Two sensor locations, the same as those used in the previous sets of experiments, were monitored. From frequency domain analysis, it has been observed that the characteristic frequencies for particle impacts are centred at 300-350 kHz, and the frequencies for bubble formations are centred at 135-150 kHz. Based upon this, two frequency bands - 100-200 kHz (E1) and 300-400 kHz (E3) - and the frequency-band energy ratio (E3/E1) have been identified as optimal for identifying particle impacts for the given system. E3/E1 > 1 has been associated with particle impacts and E3/E1 < 1 has been associated with bubble formations. By applying these frequency-band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to the number of particles and the noise levels. For example, a high threshold of 10% will clearly identify sand particle impacts, but for multiparticle tests the same threshold is unlikely to detect about 20% of lower energy particles. On the other hand, a threshold lower than 3% is likely to result in the detection of AE events with poor frequency content and incorrect classification of the weakest events. The optimal setting of the parameters used in the framework - such as thresholds, frequency bands and ratios of AE energy - is therefore likely to make identification of sand particle impacts in a laboratory environment possible within 10%. An additional advantage of this technique is that calibration of the signal levels is not required, once the optimal frequency bands and ratios have been identified

A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

Haari lainikute meetod omavõnkumiste analüüsiks ja parameetrite määramiseks

Tala on konstruktsioonielement, mille ülesandeks on vastu pidada erinevatele koormustele. Projekteerimisel alahinnatud koormused, ebatäpsused tootmisel, söövitav keskkond, konstruktsiooni vananemine ekspluatatsiooni käigus võivad talasid kahjustada ning põhjustada kogu konstruktsiooni purunemist. Seetõttu talade dünaamilise käitumise modelleerimine ja ekspluatatsiooni jälgimine on jätkuvalt aktuaalne teema konstruktsioonide mehaanikas. Käesolev väitekiri on suunatud süstemaatilisele lähenemisele võnkumiste analüüsimiseks ja purunemise parameetrite määramiseks Euler-Bernoulli tüüpi talades. Töös pakutakse välja Haari lainikute meetod sageduste arvutamiseks ja andmete töötlemiseks. Nimelt, väitekirja esimeses osas on Haari lainikuid ja nende integreerimist rakendatud vabavõnkumise ülesannete korral, kus lahendatavaks võrrandiks on muutuvate kordajatega diferentsiaalvõrrand, millel puudub analüütiline lahend (näiteks ebaühtlase ristlõikega tala, materjali funktsionaalse gradientjaotusega tala). Arvutused kinnitasid, et pakutud lähenemisviis on kiire ja täpne vabavõnkumiste sageduste arvutamisel. Väitekirja teine osa käsitleb vabavõnkumisega seotud pöördülesandeid: pragude, delaminatsioonide, elastsete tugede jäikuse, massipunktide parameetrite määramist modaalsete omaduste kaudu. Kuna purunemise asukoha ja ulatuse arvutamine võnkumise diferentsiaalvõrrandist ei ole analüütiliselt võimalik, kasutatakse antud töös tehisnärvivõrke ja juhumetsi. Andmekogumite genereerimiseks lahendati võnkumise võrrand ning tulemusi töödeldi Haari lainikute abil. Arvutused näitasid, et Haari lainikute abil genereeritud andmekogumite arvutamiseks kuluv aeg oli üle kümne korra väiksem kui vabavõnkumiste sagedustele põhinevate andmekogumite arvutusaeg; Haari lainikute abil genereeritud andmekogumid ennustasid paremini purunemise asukohta, samas vabavõnkumiste sagedused olid tundlikumad purunemise ulatuse suhtes; enamikel juhtudel andsid tehisnärvivõrgud sama täpseid ennustusi kui juhumetsad. Töös pakutud meetodeid ja mudeleid saab kasutada teistes teoreetilistes ülesannetes vabavõnkumiste ja purunemiste uurimiseks või rakendada talade purunemise diagnostikas.A beam is a common structural element designed to resist loading. Underestimated loads during the design stage, looseness during the manufacturing stage, corrosive environment, collisions, fatigue may introduce some damage to beams. If no action is taken, the damage can turn into a fault or a breakdown of the whole system. Hereof, the entirety of beams is a crucial issue. This dissertation proposes a systematic approach to vibration analysis and damage quantification in the Euler-Bernoulli type beams. The solution is sought on the modal properties such as natural frequencies and mode shapes. The forward problem of the vibration analysis is solved using the Haar wavelets and their integration since the corresponding differential equations do not have an analytical solution. Multiple numerical examples indicate that the proposed approach is fast and accurate. Damage quantification (location and severity) of a crack, a delamination, a point mass or changes in the stiffness coefficients of elastic supports on the bases of the modal properties is an inverse problem. Since it is not analytically possible to calculate the damage parameters from the vibration differential equation, the task is solved with the aid of artificial neural networks or random forests. The datasets are generated solving the vibration equations and decomposing the mode shapes into the Haar wavelet coefficients. Multiple numerical examples indicate that the Haar wavelet based dataset is calculated more than ten times faster than the frequency based dataset; the Haar wavelets are more sensitive to the damage location, while the frequencies are more sensitive to the damage severity; in most cases, the neural networks produce as precise predictions as the random forests. The results presented in this dissertation can help in understanding the behaviour of more complex structures under similar conditions, provide apparent influence on the design concepts of structures as well as enable new possibilities for operational and maintenance concepts.https://www.ester.ee/record=b539883