Rancang Bangun Sistem Distribusi Grease Secara Otomatis Dengan Metode Penjadwalan

Penelitian ini merupakan program kerja sama antara Politeknik Industri Logam Morowali dengan kawasan industri  dalam bidang preventive maintenance. Preventive maintenance merupakan salah satu upaya yang dapat dilakukan dalam menjaga kestabilan sistem produksi. HAPL atau Hot Annealing Pickling Line merupakan perusahaan yang bergerak dalam pengolahan gulungan baja hitam menjadi gulungan baja putih tahan karat. Proses pemurnian baja hitam menjadi baja tahan karat melalui proses yang panjang di mana peran motor listrik sangat lah penting. Motor listrik harus mendapatkan perhatian khusus dalam hal perawatan. Salah satu perawatan motor listrik yang harus dilakukan yaitu pengisian grease. Oleh karena itu, penelitian ini bertujuan untuk membuat sistem yang dapat mengontrol waktu pengisian grease dan lama pengisian grease yang disajikan dalam bentuk menu-menu pilihan yang terdapat pada LCD. Pada penelitian ini dikendalikan oleh Arduino Uno sebagai mikrokontroler, LCD 20x4 sebagai tampilan, RTC sebagai penyimpan waktu, keypad sebagai input pengontrol dan relay sebagai output yang terhubung dengan valve penumatik untuk mengontrol angin menuju pompa grease. Sistem penjadwalan menggunakan counter waktu yang akan menghitung durasi delay  system dalam memompa grease. Pengujian dilakukan pada motor 22KW dengan hasil jarak interval waktu pengisian grease 3204 jam, banyak grease yang harus diisi sebanyak 10,35 gram dengan lama pengisian 23 detik  dengan nilai error yang diperoleh dari pengujian 0.89%This research is a collaborative program between the Morowali Metal Industry Polytechnic and industrial areas in the field of preventive maintenance. Preventive maintenance is one effort that can be done in maintaining the stability of the production system. HAPL or Hot Annealing Pickling Line is a company engaged in processing black steel coils into white stainless steel coils. The process of refining black steel into stainless steel goes through a long process where the role of the electric motor is very important. Electric motors must receive special attention in terms of maintenance. One of the electric motor maintenance that must be done is filling the grease. Therefore, this study aims to create a system that can control the grease filling time and grease filling time which are presented in the form of menu options found on the LCD. In this study it was controlled by Arduino Uno as a microcontroller, 20x4 LCD as a display, RTC as a time saver, a keypad as input controller and a relay as an output connected to a pneumatic valve to control the wind to the grease pump. The scheduling system uses a timer that will calculate the duration of the system delay in grease consolidation. The test was carried out on a 22KW motor with the result that the time interval for filling grease was 3204 hours, the amount of grease that had to be filled was 10.35 grams with a filling time of 23 seconds with an error value obtained from the test of 0.89%

Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction

Practical and Adaptable Applications of Goal Programming: A Literature Review

Goal programming (GP) is an important optimization technique for handling multiple, and often conflicting, objectives in decision making. This paper undertakes an extensive literature review to synthesize key findings on the diverse real-world applications of GP across domains, its implementation challenges, and emerging directions. The introduction sets the context and objectives of the review. This is followed by an in-depth review of literature analyzing GP applications in areas as varied as agriculture, healthcare, education, energy management, supply chain planning, and macroeconomic policy modeling. The materials and methods provide an overview of the systematic literature review methodology. Key results are presented in terms of major application areas of GP. The discussion highlights the versatility and practical utility of GP, while also identifying limitations. The conclusion outlines promising avenues for enhancing GP modeling approaches to strengthen multi-criteria decision support

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, including neural networks, operate as black boxes: despite performing suitably well as predictive models, they are not able to identify causal associations within the data. For data-driven system to approach human-level intelligence in generating effective maintenance strategies, it is integral to discover hidden knowledge in the operational data. In this paper, we apply deep learning to discover causal relationships between multiple features (confounders) in SCADA data for faults in various sub-components from an operational turbine using convolutional neural networks (CNNs) with attention. Our technique overcomes the black box nature of conventional deep learners and identifies hidden confounders in the data through the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind turbine's operational status, and show that our technique contributes to explainable AI for wind energy applications by providing transparent and interpretable decision support

Combination of thermal modelling and machine learning approaches for fault detection in wind turbine gearboxes

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability

Subsea power cable health management using machine learning analysis of low frequency wide band sonar data

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables

Water quality indicator interval prediction in wastewater treatment process based on the improved BES-LSSVM algorithm

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.Peer ReviewedPostprint (published version

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario