Ograniczanie wyników
Czasopisma help
Autorzy help
Lata help
Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 97

Liczba wyników na stronie
first rewind previous Strona / 5 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  condition monitoring
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 5 next fast forward last
EN
Brush deburring requires consistent contact pressure between brush and workpiece. Automating adjustments to control contact pressure has proven difficult, as the sensors available in machine tools are usually not suitable to observe the small amplitude signals caused by this low force process. Additionally, both the power consumption and the vibration signal caused by the process strongly depend on the workpiece surface features. This paper describes a test setup using an instrumented tool holder and presents the corresponding measurement results, aiming to quantify the axial feed of the brush. It also discusses the interpretation of different signal components and provides an outlook on the utilization of the data for tool wear estimation.
EN
The popularity of high-efficiency permanent magnet synchronous motors in drive systems has continued to grow in recent years. Therefore, also the detection of their faults is becoming a very important issue. The most common fault of this type of motor is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that facilitate damage detection in its early stages. This paper presents the effectiveness of spectral and bispectrum analysis application for the detection of stator winding faults in permanent magnet synchronous motors. The analyzed diagnostic signals are stator phase current, stator phase current envelope, and stator phase current space vector module. The proposed solution is experimentally verified during various motor operating conditions. The object of the experimental verification was a 2.5 kW permanent magnet synchronous motor, the construction of which was specially prepared to facilitate inter-turn short circuits modelling. The application of bispectrum analysis discussed so far in the literature has been limited to vibration signals and detecting mechanical damages. There are no papers in the field of motor diagnostic dealing with the bispectrum analysis for stator winding fault detection, especially based on stator phase current signal.
EN
Interpretation of sensor data from machine elements is challenging, if no prior knowledge of the system is available. Evaluation methods must adapt surrounding conditions and operation modes. As supervised learning models can be time-consuming to set up, unsupervised learning poses as alternative solution. This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time. The approach is used to identify changing machine element states such as operating conditions and undesired changes, like incipient damage or wear. We show that knowledge about the evolving clusters can be used to identify operation and failure events. The approach is validated for machine elements with slide and roll contacts, such as ball screws and bearings. The data used has been captured using vibration and acoustic emission sensors. The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.
EN
Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.
EN
Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.
EN
In machining applications predominantly for automated machining cells, tool life is often not used to its full extend and cutting tools are exchanged prematurely to avoid tool breakage and thus machine downtime or even damage at work piece or machine. Both effective process monitoring and adequate process control require reliable data from sensors and derived indicators that enable meaningful evaluation. Acceleration measurement by the instrumented tool holder provides signals with high quality from close to the cutting zone. Using the monitoring system, the gained data of the instrumented tool holder can be analyzed especially for the use case of unexpected tool wear, chipping of the cutting edge or breakouts at end mills. This paper describes the data analysis based on the rotational sensor and the corresponding effects on the measurement, an advanced assessment of the spectral distribution in the frequency domain and the experimental results of a test series.
EN
Managed Pressure Drilling (MPD) is a technology that allows for precise wellbore pressure control, especially in formations with uncertain geomechanics. The Rotating Control Device (RCD) is a crucial part of the MPD equipment but is prone to failure. Therefore, a new condition monitoring system was developed to improve the reliability of RCDs and eliminate their catastrophic failures during MPD jobs. Non-intrusive sensors were selected during the design of this condition monitoring system. Sensors measure: vibrations, acoustic emissions, rotation, pipe movement, temperatures, and contamination level in the coolant fluid. The system can display the measurements in real-time to the operator, giving early warnings to prevent the RCD’s catastrophic failures during the job. Additionally, the data is recorded to allow further processing and analysis using ML and AI techniques.
EN
An advanced milling machine multi-sensor measurement system as a condition monitoring tool was presented. It was assumed that the data collected from the 3-axis force and torque sensor can be used as a new approach and an alternative to the typical vibration signal based health monitoring and remaining useful life prediction (RUL), when integrated with machine learning techniques that are regarded as a powerful solution. Measurement system integration with the proposed signal processing method based on decision trees with different types and levels of wavelets for the cutter reliability decision-making process was presented together with proving their ability to trace the tool condition accurately. Prediction errors achieved with the use of different signal sources and data processing methods were presented and compared.
EN
Finding a reliable machines condition monitoring technique has been attracted many researchers to avoid the sudden failure in machines and the unexpected consequences. This work proposes a fault diagnosis of air compressors using frequency-based features and distance metric-based classification. The analyzed experimental datasets contain one healthy condition and seven different fault conditions. Features are extracted from the frequency spectrum, then the best feature sets are selected using MRMR algorithm and eventually the classification is conducted using a distance metric classifier. The results demonstrated the automatic classification with more than 97% correct classification rate. The effect of selected feature set size, training sample size on the classification accuracy is also investigated. From the results, this method of analysis can be used for early detection of faults with very great accuracy.
EN
A graph of the changes in an engine’s operating speed can be used to assess the quality of the combustion in its cylinders. In this paper, the authors carried out tests on a Buckau-Wolf R8VD-136 ship engine, which was directly driving the propeller. This engine is owned by the Laboratory of Marine Engine Rooms at the Maritime University of Szczecin. For standard rotational speeds ranging from 200 to 280 rpm, with increments of 20 rpm, the authors measured the changes in the instantaneous speed for the engine’s normal operating conditions (reference graphs) as well as with one of the cylinders being out of operation. A no-combustion situation was successively introduced into each cylinder for each preset rotational speed. The obtained graphs of the instantaneous speed were then used to determine certain quantitative indicators, which the authors believe can provide information about the technical condition of the engine. The analysis concerned the averaged graphs of the speed under the conditions set for five consecutive engine operating cycles. The indicators that were calculated included the maximum difference in the speed over the engine’s full operating cycle, the uniformity of the engine speed and the differential speed area factor, the latter a term that has been proposed by the authors. The values of the individual indicators that were obtained from the reference graphs and the graphs with no combustion in one of the cylinders were compared. All indicators are sensitive to cylinder misfire. Conclusions were then drawn on the usefulness of these indicators in assessing the condition of an engine.
EN
From a user perspective, the current development of the generic term Industry 4.0 increasingly moves its orientation towards flexible production. Due to increasingly variable products with small quantities and the resulting high degree of adaptability of a plant over its entire operating phase, the need for rapid production commissioning gives rise to the demand for live commissioning support and technology evaluation of induced production start-ups. Classification axioms can be formed by 1-class learning procedures for the predictive state evaluation of subsequent production start-ups based on collected machine and process data from past production start-ups. The starting point is an adaptive algorithm that performs a dynamic tolerance band formation based on different criteria, emphasizing on adaptive characteristic segmentation. This first step represents comprehensive condition monitoring. Based on this algorithm, correlation considerations can be performed on the data structure, the measured variables, and the diagnostic parameters. Moreover, the structure of production systems can and should be included in the analyzation, so that probabilistic causalities can be postulated and then be added to the underlying data sets for quantification. Using these adaptive structure-based segmentations is the first step to interpret data sets of new production systems without the need for complex pre-configuration.
EN
Worm gearboxes (WG) are often preferred, because of their high torque, quickly reducing speed capacity and good meshing effectiveness, in many industrial applications. However, WGs may face with some serious problems like high temperature at the speed reducer, gear wearing, pitting, scoring, fractures and damages. In order to prevent any damage, loss of time and money, it is an important issue to detect and classify the faults of WGs and develop the maintenance plans accordingly. The present study addresses the application of the deep learning method, convolutional neural network (CNN), in the field of thermal imaging that were gathered from a test rig operating on different loads and speeds. Deep learning approaches, have proven their powerful capability to exploit faulty information from big data and make intelligently diagnostic decisions. Studies concerning the condition monitoring of WGs in the literature are limited. This is the first study on WGs with infrared thermography rather than vibration and sound measurements which have some deficiencies about hardware requirements, restricted measurement abilities and noisy signals. For comparison, CNN was also trained, with vibration and sound data which were collected from the healthy and faulty WGs. The results of fault diagnosis show that thermal image based CNN model on WG has achieved 100% success rate whereas the vibration performance was 83.3 % and sound performance was 81.7%. As a result, thermal image based CNN model showed a better diagnosing performance than the others for WGs. Moreover, condition monitoring of WGs, can be performed correctly with less measurement costs via thermal imaging methods.
PL
W wielu zastosowaniach przemysłowych preferuje się przekładnie ślimakowe, ze względu na ich wysoki moment obrotowy, możliwość szybkiej redukcji prędkości i dobrą sprawność zazębienia. Jednakże przekładnie tego typu narażone są często na poważne problemy, takie jak wysoka temperatura przy reduktorze prędkości czy też zużycie, pitting (wżery), zatarcie, pęknięcie lub uszkodzenie kół zębatych. Zapobiec takim uszkodzeniom, i związanym z nimi stratom finansowym i czasowym, można poprzez wykrywanie i klasyfikowanie błędów przekładni i odpowiednie opracowanie planów konserwacji. Niniejsze badanie dotyczy zastosowania metody głębokiego uczenia oraz splotowych sieci neuronowych (SSN) do monitoringu stanu przekładni na podstawie termogramów zarejestrowanych na stanowisku testowym pracującym przy różnych obciążeniach i prędkościach. Podejścia oparte na uczeniu głębokim umożliwiają efektywne wykorzystanie informacji o błędach pochodzących z dużych zbiorów danych i podejmowanie trafnych decyzji diagnostycznych. Niewiele z dostępnych publikacji poświęconych jest monitorowaniu stanu przekładni ślimakowych. Niniejsza praca jako pierwsza przedstawia badania przekładni ślimakowej z zastosowaniem termografii zamiast zwyczajowo prowadzonych pomiarów drgań i dźwięku, które mają pewne wady dotyczące wymagań sprzętowych, ograniczonych możliwości pomiarowych i głośności sygnałów. SNN opartą na danych termicznych porównano z siecią, którą uczono na zbiorach danych wibracyjnych i akustycznych pochodzących z prawidłowo działających i uszkodzonych przekładni ślimakowych. Wyniki diagnostyki uszkodzeń pokazują, że model SSN przekładni ślimakowej oparty na obrazie termicznym osiągnął stuprocentową (100%) skuteczność, podczas gdy skuteczność modeli opartych na danych wibracyjnych i akustycznych wyniosła, odpowiednio, 83,3% i 81,7%. Tym samym, model SNN oparty na obrazie termicznym pozwalał na trafniejsze diagnozowanie przekładni ślimakowej niż pozostałe modele. Ponadto zastosowanie metod opartych na termografii pozwala na poprawne monitorowanie stanu przy niższych kosztach pomiaru.
EN
The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions, while in other cases the oil passed the ideal change point.
PL
Bezpieczeństwo i wydajność silników takich, jak silniki Diesla czy gazowe, a nawet turbiny wiatrowe, zależą od jakości i stanu oleju smarowego. Stanu oleju silnikowego ocenia się na podstawie ponad dwudziestu zmiennych, z których każda ulega wahaniom w zależności od typu i zachowania silnika oraz innych czynników. W niniejszym artykule opisano model, który pozwala na automatyczną klasyfikację stanu oleju, z wykorzystaniem sztucznych sieci neuronowych i analizy składowych głównych. Badania przeprowadzono na podstawie danych uzyskanych od dwóch przewoźników pasażerskich działających na terenie jednego z krajów położonych na południu Europy. Wyniki pokazują, że każda z monitorowanych zmiennych ma znaczenie dla określenia idealnego czasu na wymianę oleju. Podczas gdy w wielu przypadkach w badanych przedsiębiorstwach możliwe było zwiększenie odstępów czasowych między działaniami konserwacyjnymi, w innych, idealny moment wymiany oleju został przekroczony.
PL
Z regulaminów wywieszonych na większości placów zabaw jasno wynika, że pełną odpowiedzialność za bawiące się dziecko ponoszą rodzice lub jego opiekunowie. Jednak za stan techniczny całego placyku odpowiada jego administrator, a urządzenia na nim się znajdujące podlegają regularnym kontrolom.
EN
In industrial field, there is an increasing demand for monitoring systems enabling predictive maintenance programs. In this context, the present work concerns the monitoring of distributed wear (pitting) in planetary gearboxes. For this purpose, some metrics of the synchronous average of the vibration signal, based on the statistical moment of the fourth order, are present in literature; in this paper, a new indicator is proposed, namely NA4mod. The effectiveness of this metric in identifying the early stage of pitting has been evaluated by conducting an accelerated life test of about 700 hours on a test bench using a back-to-back configuration. The paper introduces the proposed metric, describes the test, presents and dis-cusses the results. Metric NA4mod exhibits satisfactory capability to detect pitting with better reliability than other metrics in literature. In addition, the metric is shown to be sensitive to both early stage damage and pitting severity in the final stage. Results are verified by means of wavelet-transform analysis.
EN
Circuit Breakers (CBs) play an important role in ensuring the safe operation of protection systems. Condition Monitoring (CM) devices are widely implemented to extend lifetime, and to improve the maintenance quality. The present paper proposes a cost-based prioritization approach for CBs in a network equipped with CM devices. To this end, a mathematical formulation is developed for the categorization and modeling of equipment failures based on their severity. This formulation quantifies the effect of the CM devices on the outage rate of the equipment. The reliability parameters of the substations 400/132/20 KV, including the failure rate, λ, average repair time, r, average outage time, U, substations, in two status of without CM and with CM of the CBs are calculated. These parameters are calculated implementing a minimal cut-set method. The outage rate of equipment with and without the CM devices is used to determine the effect of the CM devices on the reliability of the network. Finally, the prioritization of substations to install the CM devices on the CBs has been investigated in terms of the Expected Energy Not Supplied (EENS) and costs of CM. To verify the effectiveness and applicability of the method, the proposed approach is applied to the CBs in the power transmission network in the Khorasan Regional Electricity Company (KREC) in Iran.
EN
Today's highly automated manufacturing specifies the service time of a tool in a way that the tooling costs are balanced against the potential costs of a tool failure. However, the potential cost induced by a tool malfunctioning are rather high. Therefore, the current state-of-the art tackles this issue by replacing the tools prematurely at fixed intervals. To tap into the potential of under-utilized tool runtime this work purposes the use of sensory-tool holders and an interfering feedback loop to the machine tool control system. Besides its real-time closed loop control, to avoid tool failure, it also provides data in the context of (a) the work order, (b) the produced part, (c) the NC-block and command line, on (d) specific machines. Based on this data an ex-post analysis to optimize tool-life and productivity scenarios becomes possible, e.g. custom NC-programs for certain work-orders, configurations and machines. Furthermore, downstreamed work steps can be changed e.g. only to measure produced workpieces if abnormal vibrations are reported by in-process-monitoring.
EN
With the passage of time of exploitation of means of technological transport, their degradation takes place and the threat to operational safety increases. The source of development of fatigue damages of gantry crane girders are areas of stress concentration caused by loads. The subject of the publication is to determine the possibility of diagnosing potential damage sites of the overhead travelling crane (girders) by magnetic metal memory (MPM). As a result of the test with the use of the TSC-7M-16 ferrite magnetometer, stress concentration areas were determined in which processes leading to the reduction of material strength or damage to the material structure may take place. Residual tangential magnetic field distributions and normal components of their gradients were determined. A magnetogram database for the needs of girder diagnostics was created.
PL
Z upływem czasu eksploatacji środków transportu technologicznego, następuje ich degradacja i wzrasta zagrożenie bezpieczeństwa eksploatacyjnego. Źródłem rozwoju uszkodzeń typu zmęczeniowego dźwigarów suwnic pomostowych są obszary koncentracji naprężeń wywołane obciążeniami. Tematem publikacji jest określenie możliwości diagnozowania miejsc potencjalnych uszkodzeń elementów konstrukcji suwnic pomostowych (dźwigarów) metodą magnetycznej pamięci metalu (MPM). W wyniku przeprowadzonego badania z użyciem magnetometru ferrytycznego typu TSC-7M-16 określono obszary koncentracji naprężeń, w których mogą zachodzić procesy prowadzące do zmniejszenia wytrzymałości materiału bądź uszkodzeń struktury materiału. Określono rozkłady resztkowego pola magnetycznego stycznego i normalne składowe ich gradientów. Utworzono bazę danych magnetogramów dla potrzeb diagnostyki dźwigarów.
EN
The article discusses the process of selecting points on a rail vehicle in which sensors recording signals will be located, with a view to their later use in the process of monitoring the condition of the vehicle and in particular elements of the first and second degree suspension system. The number of such points and their location is significant considering the complexity of the monitoring system and thus the costs of its construction and subsequent operation, as well as the possibility of using registered signals in the process of diagnosing the technical condition of the vehicle, bearing in mind the functioning of such a system in online mode.
PL
W artykule omówiony został proces wyboru punktów na pojeździe szynowym, w których ulokowane będą czujniki rejestrujące sygnały, mając na uwadze późniejsze ich wykorzystanie w procesie monitorowanie stanu pojazdu a w szczególności elementów układu podatnego I i II stopnia usprężynowania. Liczba takich punktów oraz ich rozmieszczenie ma istotne znaczenie biorąc pod uwagę złożoność systemu monitorowania a tym samym koszty jego budowy i późniejszej eksploatacji a także możliwość wykorzystania zarejestrowanych sygnałów w procesie diagnozowania stanu technicznego pojazdu mając na uwadze funkcjonowanie takiego systemu w trybie on-line.
EN
Condition monitoring and prognosis is a key issue in ensuring stable and reliable operation of mechanical transmissions. Wear in a mechanical transmission, which leads to the production of wear particles followed by severe wear, is a slow degradation process that can be monitored by spectral analysis of oil, but the actual degree of degradation is often difficult to evaluate in practical applications due to the complexity of multiple oil spectra. To solve this problem, a health index extraction methodology is proposed to better characterize the degree of degradation compared to relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfils its function. The health index is extracted using a weighted average method with selection of degradation data with allocation steps for weight coefficients that lead to a reasonable mechanical transmission degradation model. First, the degradation data used as input are selected based on source entropy which can describe the information volume contained in each set of spectral oil data. Then, the weight coefficient of each set of degradation data is modelled by measuring the relative scale of the permutation entropy from the selected degradation data. Finally, the selected degradation data are fused, and the health index is extracted. The proposed methodology was verified using a case study involving a degradation dataset of multispectral oil data sampled from several power-shift steering transmissions.
PL
Monitorowanie i prognozowanie stanu to kluczowa kwestia dla zapewnienia stabilnej i niezawodnej pracy przekładni mechanicznych. Zużycie w przekładni mechanicznej, które prowadzi do wytwarzania cząsteczek zużycia a następnie ciężkiego zużycia, to proces powolnej degradacji, który może być monitorowany poprzez analizę widmową oleju, ale rzeczywisty stopień degradacji często trudno jest ocenić podczas praktycznego użytkowania z uwagi na złożoność wielu widm oleju. W celu rozwiązania powyższego problemu, zaproponowano metodologię ekstrakcji wskaźnika stanu technicznego, aby lepiej scharakteryzować stopień degradacji niż polegając wyłącznie na danych widmowych oleju; pozwala to na dokładne prognozowanie czasu uszkodzenia, gdy przekładnia przestanie spełniać swoją funkcję. Wskaźnik stanu technicznego ekstrahowany jest za pomocą metody średniej ważonej z wyborem danych o degradacji i etapami alokacji dla współczynników wagowych, dając w efekcie odpowiedni model degradacji przekładni mechanicznej. W pierwszym etapie, dane degradacji stosowane jako dane wejściowe wybierane są na podstawie entropii źródłowej, która może opisywać zakres informacji zawarty w każdym zbiorze danych widmowych oleju. Następnie współczynnik wagowy każdego zestawu danych nt. degradacji modelowany jest przez pomiar względnej skali entropii permutacji z wybranych danych degradacji. Na koniec, wybrane dane degradacji są integrowane i ekstrahowany jest wskaźnik stanu technicznego. Zaproponowana metodologia została zweryfikowana przy użyciu studium przypadku obejmującego zbiór wielowidmowych danych dotyczących degradacji oleju pobranego z kilku przekładni kierowniczych wspomaganych.
first rewind previous Strona / 5 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.