Browsing by Author "Ompusunggu, Agusmian Partogi"
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Item Open Access Detection, prognosis and decision support tool for offshore wind turbine structures(MDPI, 2022-11-24) Vasquez, Sandra; Verhelst, Joachim; Brijder, Robert; Ompusunggu, Agusmian PartogiCorrosion is the leading cause of failure for Offshore Wind Turbine (OWT) structures and it is characterized by a low probability of detection. With focus on uniform corrosion, we propose a corrosion detection and prognosis system coupled with a Decision Support Tool (DST) and a Graphical User Interface (GUI). By considering wall thickness measurements at different critical points along the wind turbine tower, the proposed corrosion detection and prognosis system—based on Kalman filtering, empirical corrosion models and reliability theory—estimates the Remaining Useful Life of the structure with regard to uniform corrosion. The DST provides a systematic approach for evaluating the results of the prognosis module together with economical information, to assess the different possible actions and their optimal timing. Focus is placed on the optimization of the decommissioning time of OWTs. The case of decommissioning is relevant as corrosion—especially in the splash zone of the tower—makes maintenance difficult and very costly, and corrosion inevitably leads to the end of life of the OWT structure. The proposed algorithms are illustrated with examples. The custom GUI facilitates the interpretation of results of the prognosis module and the economical optimization, and the interaction with the user for setting the different parameters and costs involved.Item Open Access An improved first-principle model of AC powered solenoid operated valves for maintenance applications(Elsevier, 2023-04-05) Tod, Georges; Ompusunggu, Agusmian Partogi; Hostens, ErikSolenoid operated valves (SOVs) are critical components in many industrial applications. There has been a continuing interest in the industry to have robust condition monitoring, prognostics and health management tools to support the condition based maintenance and predictive maintenance program for such valves. For critical assets like SOVs, it is of paramount interest to understand why a component might be declared as defective. In such a situation, a first principle-model based approach will always be preferred to a purely data-driven approach, because of its inherent interpretability. Furthermore, first principle-models typically have less free parameters than their data driven counterparts and will require less data to identify their parameters. In this paper, we present the improvement of a first-principle model of alternating current (AC) powered SOVs taking into account two important degradation effects. Using this model, we show that the state of degradation can be estimated from current and input voltage measurement signals on the solenoids. Our method is validated using data from an accelerated life test campaign on 48 identical AC-powered SOVs.Item Open Access Low-cost vibration sensor with low frequency resonance for condition monitoring of low speed bearings: a feasibility study(IET - The Institution of Engineering and Technology, 2023-09-27) Ompusunggu, Agusmian Partogi; Ruiz Carcel, CristobalCondition monitoring (CM) of rolling element bearings (REBs) rotating at low speeds poses some challenges in practice because of the low signal-to-noise ratio produced by the faults. Both vibration-based and ultrasound/acoustic emission (AE) based sensing techniques have been proposed in the literature to detect faults in rolling element bearings running at low speeds. The vibration-based technique generally works within the frequency band from 0 to 20 kHz. Meanwhile, the ultrasound/AE-based techniques work in a very high-frequency band, from 20 kHz up to 1 MHz. Consequently, processing ultrasound/AE sensor data requires more computational resources compared to processing vibration data. Moreover, the hardware investment to build an ultrasound/AE-based CM system is more expensive than that of a vibration-based CM system. Since hardware and software cost is one of the main bottlenecks of the adoption of CM systems in the industry, it is, therefore, necessary to develop a cost-effective vibration-based CM system for critical bearings mounted on low-rotational speed machines. The paper presents a feasibility study in evaluating the performance of an off-the-shelf low-cost vibration sensor (10 – 20 times cheaper than high-end vibration sensors) with a low resonance frequency (around 75 Hz) to diagnose faults on REBs operating at a low rotational speed of 30 - 60 rpm. The low-resonance frequency characteristic of the low-cost sensor allows us to acquire the data at a low sampling rate of 400 Hz. A theoretical justification of why a vibration sensor with low-resonance frequency can still be effective for low-speed bearing fault diagnosis is given. This feasibility was experimentally validated on a test rig on which an REB with a seeded fault on the outer race was tested as a case study. A high-end vibration sensor (accelerometer) acquired at a 20 kHz sampling rate was also used as a benchmark. The vibration signals measured by the low-cost and high-end sensors in four different operating conditions were analysed with the well-established envelope analysis. In addition, the high-end sensor signals were also analysed with the state-of-the-art Spectral Correlation (SC) technique to compute the Enhanced Envelope Spectrum (EES) for bearing fault detection and diagnosis. The results confirmed that the bearing fault could be successfully detected and diagnosed in all the test conditions by the low-cost sensor analysed with the envelope analysis technique. On the other hand, the high-end sensor analysed with the SC technique could only diagnose the bearing fault for the least challenging test condition. The outstanding diagnostic capability of the low-cost sensor sampled at a low sampling rate has set a milestone that would enable the future development of a low-cost CM system for low-speed bearing applications.Item Open Access Quantitative evaluation of electric features for health monitoring and assessment of AC-powered solenoid operated valves(Elsevier, 2023-11-22) Ompusunggu, Agusmian Partogi; Hostens, ErikQuantitative assessment of feature performance for health monitoring is key to feature selection. This paper illustrates the application of well-established metrics in the research community - namely, monotonicity, robustness and prognosability - to the quantitative performance assessment of features for health monitoring of alternating-current (AC) powered solenoid operated valves (SOVs). These features are extracted from voltage and current signals measured on the valves and builds on previous work of the authors. Based on these metrics, the appropriate features are selected to be used as condition indicators. The selected features are inputs to a logistic regression model to predict a health index ranging from 0 to 1, which can be easily monitored and assessed by non-experts. We demonstrated the developed methodology on the experimental data acquired from accelerated life tests on 48 identical AC-powered SOVs.Item Open Access Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches(Frontiers, 2022-09-22) Brijder, Robert; Hagen, Catalina H. M.; Cortés, Ainhoa; Irizar, Andoni; Thibbotuwa, Upeksha Chathurani; Helsen, Stijn; Vásquez, Sandra; Ompusunggu, Agusmian PartogiAs large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models.