bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Remaining Life of Turbo Pump Inducer for Spacecraft Using Cumulative Damage Model
Authors: Mori Hatsuo , Imamura Makoto
Published: November 2023

In the near future, to operate reusable spacecraft safely and efficiently, it is necessary to have failure prediction technology for reusable rocket engines. Among the components that constitute a rocket engine, the failure of the turbo pump inducer has a significant impact on missions. Therefore, we have been researching to monitor the remaining life of the inducer.
This method estimates the fluctuating stress field excited in the inducer section based on the time-series signals from pressure sensors installed upstream. It predicts the remaining life using a Rain flow model to account for high- cycle fatigue (HCF) phenomena. To validate the effectiveness of the proposed method, we attempted to acquire experimental data, particularly for the challenging pressure-stress transfer section where concrete specifications are difficult to define. Through experiments using an elemental model, we demonstrated that a certain degree of remaining life can be predicted based on measurement from the upstream sensor.
This indicates that the proposed method is a promising option for solving the remaining life prediction problem of reusable spacecraft.
Nonlinear Model Predictive Control using Neural ODE Replicas of Dynamic Simulators
Authors: Kubosawa Shumpei , Onishi Takashi , Tsuruoka Yoshimasa
Published: November 2023

We propose simulation-based nonlinear model predictive control as a first step towards autonomous decision-making for stable operation of large complex dynamical systems such as chemical plants. The effect of abrupt external disturbances should be quickly eliminated, taking into account such complex dynamic responses, to maintain stable production. In this paper, we propose a control system to eliminate these effects. The system uses engineering models, including dynamic simulators, based on chemical engineering knowledge. Dynamic simulators are generally not differentiable with respect to actions; however, differentiable models are advantageous for fast nonlinear optimization. To take advantage of both reliable dynamic simulators and differentiable models, we introduce neural ordinary differentiable equation models and clone the behaviour of simulators on them. The cloned differentiable neural replica model is then incorporated into a gradient-based nonlinear model predictive control. Evaluation of this method in a real methanol distillation plant confirms that it can significantly remove abrupt heavy rain disturbances compared to existing methods.
Efficient Inspection of Civil Engineering Structures for Railways and Roads Using Images and GNSS
Authors: Abe Tomoya , Ueda Masashi , Watanabe Yuki , Matsui Yoshiyuki , Yasuno Takato
Published: November 2023

As structures deteriorate and engineers age, efficient maintenance and management of civil engineering structures becomes increasingly important. Alternative inspection of civil engineering structures using images is being recognized as an efficient inspection method. However, acquiring images of large and extensive civil engineering structures is time and equipment-intensive, which hinders mobility and is consequently not efficient. In addition, the management of existing civil engineering structures is highly specialized and lacks scalability, which means that significant effort is required to associate images with civil engineering structures. Therefore, we decided to acquire images of large and extensive civil engineering structures with video cameras to improve mobility and efficiency. In addition, as a key to linking images and civil engineering structures, we developed a technology to record latitude and longitude information as audio data in a video using GNSS (Global Navigation Satellite System) + dual-frequency RTK (Real Time Kinematic) This facilitates the rapid comparison of images and management data for existing structures with latitude and longitude information, as well as image data management in chronological order. This results in improved productivity in image-based inspection. In addition, AI processing of image data has made it possible to sample deformed areas and analyze geographical characteristics. These combined technologies have raised expectations for preventive maintenance of structures using images.
Lessons Learned from Aircraft Component Failure Prediction using Full Flight Sensor Data
Authors: Wang Changzhou , Puigh Darren , Lei Audrey , Guo Wei , Yuan Jun , Mazarek Mark
Published: November 2023

Successful aircraft predictive maintenance relies on the accurate prediction of major aircraft component failures for operators to schedule and carry out maintenance operations before failure actually happens. In this paper, we share important lessons learned from our development of prognostics alerts using full flight sensor data, including various challenges of using big data, data quality issues, failure identification for data labeling, engineering-driven vs. data-driven methods, and aggregating alerts into actionable alerts. We also provide recommendations based on our experience with prognostic alerts developed and deployed for many airline operators.
Construction and evaluation of an anomaly detection system using System Invariant Analysis Technology(SIAT) for sound data
Authors: Soma Tomoya , Sasaki Akiko
Published: November 2023

Here is a summary of a paper that presents case studies on the application of SIAT, a machine learning technique, for anomaly detection in plants and industrial machinery, with a focus on sound-based anomaly detection as a new application of SIAT:
This paper explains case studies on anomaly detection using SIAT as a machine learning technique. SIAT is specialized in analyzing time-series data and is widely used for anomaly prediction in plants and industrial machinery. In recent years, the application of SIAT has been extended to sound-based anomaly detection, and this paper presents some case studies on this topic.
Specifically, the paper provides several examples of sound-based anomaly detection, such as detecting abnormal sounds or predicting machinery failures. In these cases, SIAT was used to analyze sound data collected from multiple sensors, and anomalies were detected successfully. The results of these anomaly detection methods were then used to take preventive measures such as maintenance or repairs, leading to improvements in productivity and safety.
This paper demonstrates the usefulness of SIAT for sound-based anomaly detection and suggests the potential for expanding the scope of SIAT’s applications.
Data-Driven Prognostics and Diagnostics of Industrial Machinery — A Turbofan Engine Case Study
Authors: Graves Russell , Pankaj Peeyush , Kuruvilla Vineet J , Johnson Rachel , Inoue Michio
Published: November 2023

A machine’s Remaining Useful Life (RUL) is the expected life or usage time remaining before the machine requires repair or replacement. In data-driven methods, typical RUL estimation is performed using models trained with health condition indicator values derived from measured system data. A significant challenge in developing an RUL estimation model is transforming large, multivariate, noisy sensor datasets into useful format(s) that make the data analysis and processing pipeline efficient and extract valuable condition indicators from the data. This work uses the N-CMAPSS dataset to explore options and implications for efficiently organizing and storing large time-series datasets to support prognostics and diagnostics applications. We extend the work to demonstrate a predictive maintenance workflow and solution to (1) detect and classify faults in a turbofan engine and (2) estimate the RUL once we detect performance degradation.
Under data engineering, we investigate the impact of various file formats and file types on memory and execution time when dealing with large datasets like N-CMAPSS. We analyze, pre-process, and extract/engineer critical features from the transformed dataset by leveraging our understanding of gas turbines' operation (e.g., Brayton Cycle). We also analyze the performance of various engine submodules for different flight phases (climb, cruise, and descent). This work also explains an approach to down-sample the time series data without losing information relevant to our goals. Using the health condition indicators derived and synthesized in the data engineering stage, we train machine learning models for diagnostics (differentiate between healthy operation and seven different types of faults in the turbofan engine) and prognostics (RUL estimation).
Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet De-Noising
Authors: Jung Wonho , Yun Sung-Hyun , Park Yong-Hwa
Published: November 2023

This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy condition. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed method’s capability to monitor the condition of ball bearings. The fault diagnosis results were compared with and without the adaptive wavelet denoising. The results show its effectiveness of the proposed fault diagnostic method using acoustic signals.
Study on Fault Diagnosis in a Spacecraft Propulsion System
Authors: Adachi Kazushi , Khan Samir , Tominaga Kohji , Omata Noriyasu , Tsutsumi Seiji , Nagata Taiichi
Published: November 2023

The propulsion system in a spacecraft is an important subsystem for orbit transfer and attitude control. A fast and accurate fault diagnosis system contributes to the safety of the entire system. As the system becomes more complex, identifying faults, their locations, and root causes becomes increasingly difficult. This study utilized Principal Component Analysis (PCA) and feature optimization with Fast Fourier Transform (FFT) analysis using greedy algorithm to achieve fault diagnosis systems for spacecraft to replace the current operation based on the expert knowledge. By applying PCA to simulation data for the faults were successfully detected and their locations and root causes identified.
Design of a Framework for Demand Forecasting Using Time Series Decomposition-Based Approach
Authors: Shibayama Hazuki , Ishigaki Aya , Kobe Takasumi , Ueda Takafumi , Arimizu Daichi , Nakamura Takaaki
Published: November 2023

In recent years, artificial intelligence (AI) has made highly accurate demand forecasting possible. However, improving forecast accuracy does not necessarily mean reducing inventory costs or improving service levels in supply chain and inventory management, which are closely related to demand forecasting. Workers require not only high accuracy but also a basis for making decisions a based on forecasts. Autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) are demand forecasting methods with high accuracy and interpretability. However, these methods cannot provide evidence for demand fluctuations such as trends and seasonality, although they exhibit an autoregressive time-series structure. In this study, a framework for demand forecasting with high accuracy and interpretability was designed using time series decomposition and ARIMA to support decision makers in demand forecasting. The Seasonal-trend decomposition using locally estimated scatterplot smoothing (STL) is used to decompose a time series into three components trend, seasonality, and residual to provide decision makers with an easily understandable basis for demand changes. In addition, the ARIMA model is used for trends and residuals to achieve highly accurate forecasts. Comparing the prediction accuracies of the proposed STL-ARIMA and SARIMA models shows that STL-ARIMA has higher interpretability than the SARIMA model and the same accuracy.
two-step detection method for actuator and sensor failures based on self-repairing PID control
Authors: Hara Masayoshi , Takahashi Masanori
Published: November 2023

Our previous works have proposed a self-repairing PID (SRPID) control for plants with sensor failures. This method uses an I-controller (integrator) with a well-designed auxiliary signal to detect failures. However, the SRPID has not addressed actuator failures. This paper presents a two-step detection method for actuator and sensor failures based on the SRPID and confirms the effectiveness of the proposed detection method through experiments on a ball-and-beam system.
Time Shifting Data Augmentation to Alleviate Class-Imbalance Problem for Cross-Domain Bearing Fault Diagnosis
Authors: Yoo Donghwi , Choi Minseok , Oh Hyunseok
Published: November 2023

This paper presents a new cross-domain fault diagnostic method for rolling element bearings with class-imbalanced datasets. The key idea to alleviate the class imbalance problem is the incorporation of the data augmentation strategy. This study proposes a new data augmentation technique, namely, time shifting data augmentation (TS- DA). Synthetic data is generated to balance the number of normal and fault data. The validity of the proposed method is evaluated using a dataset from the bearing testbed. The results show that the proposed method augments different types of bearing fault data effectively and outperforms existing methods under the class imbalance problem.
Hybrid Approach of XGBoost and Rule-based Model for Fault Detection and Severity Estimation in Spacecraft Propulsion System
Authors: Lee Sang Kyung , Lee Jiwon , Lee Seungyun , Kim Bongmo , Kim Yong Chae , Lee Jinwook , Youn Byeng Dong
Published: November 2023

This study presents a method for fault detection and severity estimation of the spacecraft propulsion system. The spacecraft propulsion system is complicated, consisting of many valves, and operates in a harsh environment. Therefore, faults due to external factors such as bubbles or valve breakage can occur within the complex system at any time. To diagnose faults in this system, we propose a hybrid method of XGBoost-based method and rule-based method. In the XGBoost-based method, the overall fault classification, including unknown fault filtering was performed. In addition, the rule-based model was performed to estimate the fault severity. The results show that the proposed method reached a 99.94% score, which is calculated by the score matrix considering fault classification accuracy and severity estimation.
Long-Term Preventive Failure Mitigation Strategy For Transformers Based on Markov Method
Authors: Wang Jianzheng , Hu Guoqiang , Cho Sungin
Published: November 2023

In this paper, we propose a preventive failure mitigation strategy in the power system based on Markov method. Specifically, we consider multiple units in the system, which are of different types and are managed by a single utility company. To characterize the operation, failure mitigation, and deterioration processes of the equipment, a continuous-time Markov model is formulated. By modelling the failure rate of equipment and the reinstallation rate after failures, the steady state of the proposed Markov model is analytically derived. Then to optimize the long-term net revenue of the utility company, the optimal failure mitigation rate is determined by considering the failure mitigation capacity for each equipment type as well as the overall failure mitigation capacity of the company. The performance of the proposed algorithms is demonstrated with three types of transformers in the simulation.
Advanced Weibull Modelling with Outliers
Authors: Pang Yipeng , Hu Guoqiang , Cho Sungin
Published: November 2023

This paper presents a comprehensive process for the advanced Weibull modelling with potential outlier inclusions. In this process, an algorithm is designed to identify if there exist any outliers (i.e., failures with different failure modes from the majority) in the failure data of the equipment of interest. Depending on the conditions of the identified outliers, a suitable statistical model is developed. To validate the model, it is compared with the estimated empirical distribution function with the inclusion of new failure data. It is shown that the proposed advanced Weibull model outperforms the two-parameter Weibull model in terms of fitting, and hence a better accuracy is achieved in the failure statistical analysis. Case study in the application of power systems is conducted to illustrate its effectiveness.
Tacholess Instantaneous Speed Estimation Considering the Characteristics of Vibration Harmonics
Authors: Yoo Jinoh , Park Jongmin , Kim Taehyung , Ha Jong Moon , Youn Byeng Dong
Published: November 2023

Knowledge of instantaneous shaft speed is vital for non- stationary condition monitoring of rotating machinery in real applications. To avoid installing expensive and inconvenient encoders, many researchers have developed instantaneous speed estimation methods by extracting the shaft speed from vibration signals. However, previous methods show limitations due to challenges in vibration signals. Therefore, we propose a novel instantaneous speed estimation method considering the characteristics of vibration harmonics to overcome the difficulties. Multiple harmonic components and their characteristics are utilized to obtain an accurate ridge in the time-frequency representation (TFR). The proposed method is validated and compared with the previous methods using a gear vibration simulated signal and civil aircraft engine dataset. The results show the accuracy and robustness of the proposed method.
Study of Localization of Partial Discharges in Oil-filled Transformers using Acoustic Emission Signals
Authors: Otake Yasutomo , Tajiri Kunihiko
Published: November 2023

The measurement of partial discharges (PD) in power transformers is crucial for fault detection and maintenance scheduling. In this paper, the relationship between detection intensity, type of PD source and propagation distance is investigated using an acoustic emission (AE) sensor. The AE wave intensity by the corona discharges are relatively strong. Creeping discharges were next, followed by PD in bubbles. Furthermore, two methods for calculating time difference of arrival (TDOA) in locate calculations, energy reference and generalized cross-correlation (GCC), were experimentally compared. The results showed that the energy reference method is suitable when sensors can be placed around the tank, while the GCC method is suitable when sensors are concentrated in specific parts of the tank. This finding may contribute to improving the accuracy of maintenance diagnostics.
SKYWISE - Big data platform as a foundation of airlines predictive and health monitoring
Authors: Bernard William , Hoffmann Anthony
Published: November 2023

By abstracting the complexity of data collection, combining engineering and operational data, and enabling collaboration across the industry in a rich analytical environment, airlines overcome intrinsic challenges of the predictive and health monitoring such as data silo’s, complex data management and data-driven decision enablers. Make the data visible and understandable through strong and flexible data integration. Make data actionable through digital twin and collaboration. Make data proactive by implementing predictive models and bringing machine learning into operations. Airbus Skywise, powered by Palantir Technologies and fueled by Airbus expertise, provides Airlines with the leading aviation data platform to address aircraft operation challenges.
Energy Saving Structural Health Monitoring Using Semi-Active Identification
Authors: Hara Yushin , Tang Tianyi , Otsuka Keisuke , Makihara Kanjuro
Published: November 2023

This paper presents a novel approach to achieving system identification of a structure while minimizing energy consumption. The identified structural model can be used for structural health monitoring. In the aerospace environment, energy consumption is strictly regulated. To address this issue, we propose an energy-saving identification method that utilizes piezoelectric semi-active control as an input generation technique. This approach generates control force through electric switch activation, resulting in a smaller amount of energy consumption for input generation than conventional active control. We achieved semi-active input generation suitable for identification by incorporating a novel control strategy. The semi-active control has the disadvantage of limiting the free control of inputs. The identification performance may degrade if the properties of the semi-active input deviate from the desired ones. To address this issue, we also propose a data processing method that extracts a certain input with appropriate properties for identification from the acquired input. We validated the proposed method through numerical simulations and experiments. The results confirmed the feasibility of the semi-active identification method for structural health monitoring. Additionally, we found that the total energy consumption during the 20-second experiment was only 68 mJ to identify the 50 kg structure.
Field study toward anomaly road damage detection with drive recorder
Authors: Tsuchiya Masato , Miyamoto Ken , Ota Takashi , Sugama Yasushi
Published: November 2023

As one of the ways to reduce road maintenance costs, road damage detection with a mobile camera is gaining attention. Most of conventional damage detection use supervised learning, nevertheless three practical drawbacks exist. Firstly, supervised learning requires a high manual cost to collect annotated data for training. Secondly, some damages are rarely observed, resulting in imbalanced data and difficulty in training an efficient model for all damage categories. Additionally, annotators may not identify such rare damages correctly. Thirdly, supervised learning cannot detect unknown categories of damages, though unknown categories are often found in a practical scene. To overcome these three drawbacks, we propose an ensemble model that combines anomaly detection and supervised damage detection. Anomaly detection can detect previously unknown and rare types of damage, while supervised damage detection ensures damages frequently observed on roads. Two different models cover wider categories of road damages. Our ensemble model is expected to achieve higher accuracy and lower manual cost.
Quantitative Analysis of Domain Discrepancies Between General- Speed and Very Low-Speed Bearings and Its Applications to Fault Diagnosis of Very Low-Speed Bearings Using Transfer Learning
Authors: Lee Seungyun , Kim Sungjong , Kim Su J. , Lee Jiwon , Yoon Heonjun , Youn Byeng D.
Published: November 2023

This paper proposes a fault diagnosis framework for very low-speed bearings using transfer learning. To handle large domain discrepancies between general-speed and very lowspeed bearings, the quantitative analysis is performed using operating information and geometries of bearings. From this analysis, the domain discrepancy can be quantitively compared under various speed conditions. Furthermore, a transfer learning technique is proposed to reduce the analyzed discrepancies. The domain discrepancy can be significantly aligned by integrating the operating information and geometries of bearings into transfer learning. Also, the proposed framework is not tailored for the specific algorithm; this means that the framework can be applied to any transfer learning technique regardless of the architecture. The proposed method is validated using two bearing datasets under general-speed and very low-speed conditions. The results show that the domain discrepancy can be quantitatively measured for transfer learning. Additionally, the proposed fault diagnosis framework outperforms the existing methods by aligning domain discrepancies of bearing datasets under large different speeds.
Trip Reduction in Turbo Machinery
Authors: Mathur Pranay , Michelassi Carlo , Karatha Simi Madhavan , Pedoto Gilda , Alguacil Miguel Gomez
Published: November 2023

Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start have major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these Critical to Quality (CTQs) are measured with 2 metrics, MTBT (Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability.Baker Hughes Trip reduction program structured to reduce these unwanted trip1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition.2. Real time alerting system based on analytics available remotely.3. Remote access to trip logs and alarms from control system to identify the cause of events.4. Continuous support to field engineers by remotely connecting with subject matter expert.5. Live tracking of key Critical to Quality (CTQs)6. Benchmark against fleet7. Break down to the cause of failure to component level8. Investigate top contributor, identify design and operational root cause9. Implement corrective and preventive action10. Assessing effectiveness of implemented solution.11. Develop analytics for predictive maintenanceWith this approach, Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This paper explains this approach while providing successful case studies, in particular where 12nos. of Liquified Natural Gas (LNG) and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT.
Power Consumption Optimization for Electric Arc Furnace with Time Series Prediction
Authors: Lee Jaehyuk , Kim Songhwan , Yoo Boseon , Choi Jaesik
Published: November 2023

Optimizing power consumption for electric arc furnace (EAF) has a critical impact for maximizing productivity. To achieve the goal, we propose an AI based algorithm that determines optimal timing for recharging scrap to EAF. More specifically, we predict power consumption and time duration required for melting scrap considering scrap types and amounts of each type of scrap. Furthermore, with the advance in explainable AI, we offer guidance for the optimal timing of recharging scrap. We evaluate the performance on a real site and successfully reduce scrap charging time of 3% and power consumption of 7.1%, 53,802 Japanese Yen.
Event detection in a noisy time series data using static smoothening and gradient variation analytics
Authors: T Saravanaram , Mankad Unnat , Karatha Simi Madhavan , Allegorico Carmine , Halaki Arati
Published: November 2023

Data-driven prognostic industrial asset health management is essential to improve reliability and availability of industrial machineries. Industrial sensors capture multiple phenomena of underlying assets like environment impact, system degradation, process variation, instrument noise, control system response or user induced actions. Some of these phenomena have distinct signature and have impact on component life and remaining useful life. Capturing these events’ signature help to apply advanced AI algorithm to categorize various failure modes and early detection. For Ex: Some of transient events can contribute to thermal cycling of component and in turn reduces life of high temperature and low thermal mass components. Due to random and nonstandard nature of these events, it is extremely challenging to detect and extract these events. Existing change point detection algorithms have limitations to detect sudden variations, which is common due to process or control actions. The noisy signal adds additional challenge to differentiate between important event and noise. In this work of time series analysis, we propose a new approach for consistent estimation of numbers and locations of the change-points. With this tunable algorithm combined with event labelling and pattern search, we can detect events of our functional needs and use them as a feature for our prediction models. This methodology has opened exciting opportunity to further analyse these events with development of classification system and time to fail prediction models and also apply large language models for time series data.
Improving Anomalous Sound Detection by Distance Matrix-Based Visualization of Measurement Flaws
Authors: Tanaka Nobuaki , Shiraga Takeru , Itani Yusuke
Published: November 2023

Although recent DNN-based methods have improved the performance of anomalous sound detection systems, it is still difficult to deploy a system in a real environment without performance degradation. This is often due to measurement flaws such as sensor variability, poor setup, or environmental noise. Since such adverse effects are difficult to model by machine learning, a practical approach to this issue is for humans to identify such flaws and correct them. To this end, we propose a method to visualize measurement flaws as a heatmap based on the distance matrix of the samples in the dataset. This method is designed to find unexpected flaws in the measurement process. Using this method, we were able to identify measurement flaws of anomalous sound detection systems in real production lines. The robustness of anomalous sound detection can be improved by correcting the flaws found by our method.
Fully Unsupervised Defect Clustering using Adversarial Autoencoder
Authors: Kim Taewan , Lee Seungchul
Published: November 2023

Identifying defect types and developing proper maintenance strategies is a major concern in modern industry. Most conventional studies have been conducted primarily based on a supervised learning scheme. However, supervised learning has a critical limitation in that it requires labeled data, which is difficult and expensive to obtain in real-world industry. Considering that there are many industries that do not perform post investigations on the defects, fully unsupervised learning methods, which do not exploit any information such as label data or the number of types, need to be developed. Accordingly, in this study, we propose a fully unsupervised defect clustering method that does not exploit any information other than the data itself. The proposed method consists of two major components. The first is dimensionality reduction into latent space via adversarial autoencoder, and the second is a Bayesian mixture model for distribution estimation in latent space. The experiments on a rolling-element-bearing dataset validate the effectiveness of our method. Specifically, our method performs defect clustering without any information other than the data itself, which is promising for real industrial applications.
Study on the Intent-based System Design Automation Method for Fault Detection and Fault Tolerance
Authors: Kuroda Takayuki , Watanabe Toshiki , Fukuda Tatsuya
Published: November 2023

ICT systems are becoming an important infrastructure indispensable for business operations in various industries. While the requirements are becoming more diverse and complex, stable service provision is required. As a result, the burden on operations is increasing, and there are growing expectations for automation technology to solve this problem. This study investigates techniques to significantly reduce the burden on operators by automating the construction and maintenance of systems that satisfy Intent, a concise description of users' requirements. However, even using the results of previous research, it has been difficult to automate operations with consideration for the stability of service provision. Therefore, this paper describes an automated method based on intent for detecting failures that occur in the target system and an automated design method for fault-tolerant systems. First, we describe the basic concept, present the results of thought experiments, and discuss its effectiveness.
Differential Diagnosis with Active Testing
Authors: Van Gerwen Emile , Barbini Leonardo , Borth Michael
Published: November 2023

The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes. We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.
Resilient Operation Planning for CubeSat Using Reinforcement Learning
Authors: Kuroiwa Shuntaro , Kogiso Nozomu
Published: November 2023

This study proposes an autonomous operation procedure for a CubeSat by applying reinforcement learning based on resilient engineering. The CubeSat requires rapid judgment in every visible window based on a sufficient understanding of the health conditions of the satellite from limited telemetry data due to the limited communication performance and poor protection functions from the harsh environment. This study first performs a risk analysis by using System Theoretic Process Analysis (STPA) to evaluate the risk scenario of the Cube-Sat. In order to successfully operate the missions while avoiding the risk scenarios, reinforcement learning is applied to learn adequate behaviors according to the satellite situations such as the temperature and voltage of the installed battery, the sunlight and eclipse phase and the mission progress and plan. Through numerical examples, the validity of the proposed method is illustrated.
Concept of Condition Monitoring for AC-DC Converter Output Capacitors via Discriminative Features
Authors: Kareem Akeem Bayo , Hur Jang Wook
Published: November 2023

This paper discusses recent research on the condition mon- itoring (CM) approach for aluminium electrolytic capacitors (AEC) used in power electronics equipment such as switched- mode power supplies (SMPS). Capacitors are identified as the most critical component with the highest percentage of failure in AEC. CM offers a better paradigm for AEC due to its long- lasting ability (endurance). This study proposes accelerated life testing through electrical stress and long-term frequency testing for the AEC component. An experiment test bench was set up to monitor the critical electrical parameters such as dissipation factor (D), equivalent series resistance (ESR), capacitance (Cp), and impedance (Z), which serve as health indicators (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique. This research contributes to developing a cost-effective CM approach for AECs used in power electronics equipment, which can reduce downtime and maintenance costs.
Study on the intermittent fault mechanism of electromagnetic relay under complex environmental stress
Authors: Jingzhong Ye , Kehong Lyu , Guanjun Liu , Jing Qiu
Published: November 2023

When the relay experiences intermittent failures, its faults are difficult to reproduce, making it difficult to repair, and bringing daunting challenges to equipment reliability and mission success. Domestic and international studies have shown that environmental stresses, especially vibration stress and temperature stress, are important causes of intermittent failures of relays, but their mechanisms are not yet clear. Therefore, an in-depth analysis of the mechanism of relay intermittent failure under complex environmental stresses has become a critical challenge that needs to be accomplished. To address this issue, this paper takes a model of electromagnetic relay as the research object and conducts an in-depth study of the intermittent failure mechanism of the relay under vibration stress and temperature stress after salt spray corrosion using the technical approach of theoretical analysis, simulation analysis and experimental verification.
Abnormal Detection Using Two-Stage Method in Combined Power Plant
Authors: Kim Yun Hee , Bae Suk Joo
Published: November 2023

Complex systems, such as power plants, demand precise and reliable anomaly detection mechanisms. Traditional supervised learning approaches often fall short due to the challenges of imbalanced data and the scarcity of labeled abnormal instances. This paper introduces a two-stage methodology to address these challenges. The first stage emphasizes feature engineering, mitigating redundant sensor effects, and reducing dimensionality through Kmeans clustering and PCA. The second stage employs an LSTM-Autoencoder for abnormal event detection. Validated using data from a combined power plant, our approach demonstrates superior performance over existing techniques in terms of accuracy and computational efficiency. This research not only advances the field of anomaly detection in power plants but also offers insights for other complex systems.
Assessing the Performance of Transformer for Time Series Anomaly Detection
Authors: Nakashima Takuto , Yairi Takehisa
Published: November 2023

This study aims to assess the effectiveness of the Transformer-based reconstruction approach for detecting anomalies in time series data. The reconstruction error-based anomaly detection method was applied to both multivariate time series from NASA SMAP/MSL and univariate time series from UCR. Four deep learning models, including Transformer, Dilated CNN, LSTM, and MLP, were compared in terms of their ability to reconstruct input data. Dilated CNN outperformed the other models in almost all experimental results, achieving a 25% higher score than Transformer on the UCR dataset when trained with random masking, and a 60% higher score when trained with middle masking. These results suggest that the Transformer did not perform as well as expected for anomaly detection based on time series reconstruction errors, and its inferiority to Dilated CNN may be attributed to the characteristics of the time series and the limited training data. Future research should focus on developing Transformer models that can better capture the properties of time series data and investigate the relationship between the model’s performance, data volume, and model complexity.
AVIATAR – Deep dive into prediction with AVIATAR with in-service examples from airlines
Authors: Lang Sebastian
Published: November 2023

This paper demonstrates how AVIATAR prediction successfully improves operational stability.It will explain a specific use case example, presented with customers to complete the holistic view on big data models and predictive recommendations through the eyes of an airline. A deep dive int he ATAchapter36 (EBASS-suite) will illustrate how Lufthansa Technik’s predictive maintenance knowledge achieves numerous cost savings and provides increased operational stability.
Configuration and Comparative Study of Prediction Models for Indoor Air Quality
Authors: Lee Geonhwi , Seo Hea-Ryeon , Choi Hae-Jin
Published: November 2023

Since COVID-19, cultural life and working conditions have changed to be done indoors. Various harmful substances are produced indoors, and when they enter the human body through the air, they can cause serious diseases. Indoor air pollution is not visible to the naked eye, and it is not easy for people to perceive it. Human damage due to harmful indoor gases is increasing. In this study, we predict indoor air pollution occurring in daily life in advance. We collected indoor air quality data every 10 seconds from the different types of residential spaces in Seoul. For accurate prediction, we compared the prediction performances of various models, such as the ARIMA model, and the recurrent neural network (RNN) based models. In addition, the prediction performances were compared according to the size of the historical window. The comparison results revealed that for short-term interval predictions, shorter historical window sizes and simpler models were more effective. This study provides a baseline for selecting a predictive model and configuring training datasets.
Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles
Authors: Oh Jeongmin , Yoo Donghwi , Oh Hyunseok , Ryu Yong Hyun , Lee Kyung-Woo , Sung Dae-Un
Published: November 2023

This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.
IMPLEMENTASI KARAKTER RELIGIUS, DISIPLIN DAN JUJUR PADA TINGKAT SMA MUHAMMADIYAH DI BANDAR LAMPUNG
Authors: Ihsan Alsefta , Nugroho Anggi Septia
Published: November 2023

Masalah terkait Pendidikan karakter merupakan usaha yang dilakukan untuk mewujudkan peradaban bangsa yang berdasarkan keteladan dan pembiasaan. Kehidupan generasi muda juga terdapat banayak masalah yang berkaitan kelemhan karakter. Serperti berbgai kasus narkoba, perkelahian antar kelompok, pergaulan bebas dan masih banyak yang lainnya yang menunjukkan penyakit sosial terkait masalah mental atau karkter. Anak-anak bangsa ini kehilangan pegangan dan arah hidup, sehingga hampa secara Religius, moral dan perilaku. Penyebanya berbagai macam seperti perpisahan orang tua, intraksi sosial yang longgar atau prinsif dan kontrol sosial yang lemah. Tujuan penelitian ini adalah mengetahui impelemtasi pendidikan karakter di Sma Muhammadiyah 2 Labuhan Ratu Bandar Lampung. Metode penelitian ini adalah kualitatif deskriptif. Disebut kualitatif karena merupakan penelitian yang bermaksud untuk memahami fenomena tentang apa yang dialami. Teknik Pengumpulan Data, Metode observasi, Interview (wawancara), Dokumentasi. Metode Analisa Data, Reduksi data, Penyajian data. Berdasarkan hasil penelitian menunjukkan bahwa: Implementasi Pendidikan Karakter di SMA Muhammadiyah 2 Labuhan Ratu Bandar Lampung yaitu: 1. Karakter religius terimplementasi dalam wujud pelaksanan shalat berjamaah dan mengaji setiap hari sebelum mulai pelajaran pertama, 2. karakter disiplin terimplementasi dalam wujud datang tepat waktu, 3. karakter jujur terimplemenntasi dalam wujud jujur saat datang terlambat.
FAKTOR-FAKTOR PENYEBAB PESERTA DIDIK TIDAK MENGGUNAKAN BUSANA SYAR’I DI KELAS VII SMP MUHAMMADIYAH 1 BANDAR LAMPUNG
Authors: Putri Intan Cempaka , Putra Arizal Eka , Machsun Muhammad
Published: November 2023

Tulisan ini secara umum akan membahas tentang bagaimana pengimplementasian dari sebuah teknik penilaian dalam suatau mata pelajaran dan menjawab perumusan masalah tentang bagaimana penerapan teknik penilaian sikap pada mata pelajaran Akidah Akhlak di MAN 2 Tanjungkarang. Tulisan ini adalah hasil penelitian lapangan dengan menggunakan metode penelitian kualitatif dengan salah satu guru Akidah Akhlak di MAN 2 Tanjungkarang yaitu bapak Rozak sebagai subjek penelitian dan implementasi teknik penilaian sikap yang dilakukan guru tersebut sebagai objeknya. Metode yang digunakan dalam pengumpulan data adalah dengan metode wawancara, metode observasi dan metode dokumentasi. Hasil analisis akhir dari implementasi rambu-rambu pelaksanaan penilaian kompetensi sikap melalui observasi yaitu guru Akidah Akhlak telah melaksanakan 7 dari 8 langkah penilaian sikap melalui teknik observasi dengan baik, maka, dapat disimpulkan teknik penilaian sikap observasi yang dilakukan oleh guru telah diimplementasikan dengan baik dan hasil nilai yang diperoleh siswa dari penilaian sikap dengan teknik observasi ini bisa dipertanggungjawabkan. Namun, penggunan teknik observasi ini hanya dipergunakan maksimal untuk mengobservasi beberapa aspek sikap saja yaitu aspek disiplin, santun, pro-aktif, dan percaya diri.Kata kunci; teknik penilaian sikap, penilaian Akidah Akhlak, teknik penilaian observasi.
EFEKTIVITAS PENDEKATAN DIFFERENTIATED INSTRUCTION DALAM PROSES PEMBELAJARAN
Authors: Pertiwi Kartika Eka
Published: November 2023

Setiap peserta didik adalah individu yang unik yang memiliki karakter masing-masing. Perbedaan karakteristik ini menjadi tantangan bagi pendidik karena kebutuhan belajarnya akan berbeda. Pendidik harus menciptakan suasana belajar yang mampu mengakomodasi perbedaan tersebut. Differentiated instruction merupakan pendekatan yang dapat menjadikan perbedaan sebagai dasar dari perencanaan pembelajaran. Perbedaan yang ada dapat digunakan sebagai kekuatan untuk membantu peserta didik lebih mudah dalam belajar. Langkah pertama dilakukan adalah mengumpulkan informasi tentang kesiapan belajar, minat, dan profil belajar peserta didik. Selanjutnya merancang konten, proses dan produk yang berbeda sesuai kebutuhan peserta didik yang didasarkan pada kesipan belajar, minat dan profil belajar peserta didik. Pada tahap terakhir yaitu menerapkan pembelajaran differentiated instruction. Pada tahap ini, setiap peserta didik dapat mengaitkan masalah dalam kegiatan pembelajaran kedalam kehidupan sehari-hari dengan materi yang diajarkan sesuai dengan minat mereka. Menganalisis dan memecahkan masalah tersebut menggunakan cara dan fokus matematis yang berbeda sesuai pengetahuan awal dan profil belajar peserta didik masing-masing.
Jihad ISIS dalam prespektif Tafsir Al- Azhar
Authors: Adnan Afton Zuhri
Published: November 2023

(pandangan Buya Hamka)