bluuebunny/crossref_metadata_2025 huggingface dataset:
Physics-based prognostics-promises and challenges
Authors: Wang Yiwei , Kim Nam H. , Haftka Raphael T.
Published: March 2024

In this paper, an interesting observation on the noisedependent performance of prognostics algorithms is presented, as well as a method of evaluating the accuracy of prognostics algorithms without having the true degradation model is presented. We found that the randomness in the noise leads to very different ranking of the algorithms for different datasets. In particular, even for the algorithm that has the best performance on average, poor results can be obtained for some datasets. In absence of true damage information, we propose a metric, mean squared discrepancy (MSD), which measures the difference between the prediction and the data. It is shown that the ranking by MSD is strongly correlated with ranking with true degradation model. This may be particularly useful when information is available from multiple sites of damage for the same application.
Ensemble Learning for Remaining Useful Life Prediction
Authors: Li Zhixiong , Hu Chao
Published: March 2024

While significant research has been conducted in modelbased and data-driven prognostics, very limited research has been done to investigate the prediction of RUL using an ensemble learning method that combines prediction results from multiple learning algorithms. This research aims to introduce a new ensemble prognostics method with degradation-dependent weights. The performance of the proposed method is evaluated by the C-MAPSS data sets.
Data-Driven Based Battery Health Prognosis with Diagnosis Uncertainties and Insufficient Training Data Sets
Authors: Xi Zhimin , Zhao Xiangxue , Hu Chao
Published: March 2024

This paper investigates data-driven based battery prognosis with diagnosis uncertainties and insufficient training data sets. Four types of data-driven prognosis methods are investigated including the neural network, similarity-based approach, relevance vector machine, and a recently developed copula-based approach. The remaining useful life (RUL) predictions of lithium-ion battery capacity are compared with capacity estimation error due to the fact that onboard lithium-ion battery capacity estimation is difficult and almost always contains estimation errors. Thus, robustness of each prognosis methods can be studied for real time capacity RUL estimation. Furthermore, collection of sufficient run-to-failure training data sets for lithium-ion batteries is almost impossible even though it is desirable for all data-driven based methods. Therefore, robustness of these methods in terms of the insufficient training data sets is also studied. These insightful results will helpdesigners choose appropriate prognosis algorithms in designing battery management systems (BMS) for lithium-ion batteries.
Novel PHM concept for future use in safety relevant electronics for harsh environment
Authors: Gromala Przemyslaw Jakub
Published: March 2024

Electronic systems are and will be the major factor that will increase the safety on the road. According to the World Health Organization up to 2020 it is expected that number of accidents will be reduced by 50%. This will be realized through introduction of more advanced safety relevant systems that will slowly take control as well as responsibility for steering the car. As of today the safety relevant systems such as ABS/ESP saved only in Germany 8500 lives, and prevent 260000 accidents. Rear-end collisions that are among the worst, can be reduced by 72% due to the emergency  braking system. In the urban traffic, up to 40 km/h the Bosch emergency braking system can completely prevent collisions with stationary vehicles. It is expected that autonomous driving will completely revolutionize the transportation system that will finally lead to 0 casualties in 2050. In order to fulfill social expectancy, the safety relevant electronics systems that will be used in future automotive industry must be more complex. The future ECUs used in self-driving cars will be typically smart systems of the 3rd generation, which will perform human like operations. The 3rd generation smart systems will act independently in respect to control and decision making. In addition, these systems will be able to self-testing, self-calibration and self-healing. Last but not least, the concept of Internet of Things will bring the components that are traditionally developed for consumer electronic market under the engine hood. All these aspects will required new approach regarding reliability and quality assurance. It is already observed that the lifetime requirements for embedded electronics used in automotive increases from 15 towards 25 years and for avionics systems towards 35 years. At the same time the time of qualification test is expected to be reduced by 30% with the cost of reliability tests to be reduced by 25%. All these challenges and requirements can be realized by development of the new reliability concept that is strongly supported through numerical simulation and product optimization at the very early development stage. In the seminar I will present the novel approach for reliability assessment of the future electronic control units and smarty systems. I will present concept of simulation driven design that we are using during the development phase as well as the application of the hybrid prognostics and health management concept for the future safety relevant electronic control modules.
Deep Learning Based Virtual Metrology and Yield Prediction in Semiconductor Manufacturing Processes
Authors: Jeong Myong Kee , Choi Jeongsub , Son Youngdoo , Kang Jihoon
Published: March 2024

We present a deep learning based supervised autoencoder to extract meaningful features from massive in-line sensor functional signals of semiconductor manufacturing processes. Based on those extract features, we build thevirtual metrology model to predict important quality characteristics of the process and the yield prediction model. A real-life case is studied in this work, and the empirical results show that the proposed model outperforms generalapproaches for the predictions using signal data.
Model- and Non-model-based Damage Detection Methods Using Vibration Data
Authors: Zhu W.D.
Published: March 2024

Modal parameters of a structure, including natural frequencies, modal damping ratios and mode shapes, are directly related to its material properties, such as mass, damping and stiffness. Changes in material properties of astructure due to occurrence of damage can result in those in its modal properties. In this work, model- and non-modelbased structural damage detection methods by use of modal parameters are introduced. The model-based method uses changes in natural frequencies of a structure; it requires an accurate physics-based model of the structure and a minimum amount of vibration measurement. The nonmodel-based methods use measured mode shapes and curvature mode shapes, and they do not require use of structural models. Experimental damage detection on a space frame, a beam and a plate are carried out to validate the model- and non-model-based methods.
Evolution of the dynamic response and its effects on serviceability of offshore wind turbines with stochastic loads and soil degradation
Authors: Nam Woochul , Oh Ki-Yong
Published: March 2024

Novel methods combined with an integrated simulation platform are suggested for the design of offshore wind turbines and substructures that ensure twenty-year lifespan. These methods enable us to estimate the long-term evolution of dynamic responses of offshore wind turbines due to the degradation of the soil modulus of the foundation under stochastic loading conditions. The results of this study show that random fluctuations of the soil stress caused by stochastic loads (i.e., aerodynamic and hydrodynamic loads acting on offshore wind turbines) can be described by a Rayleigh distribution and a Gaussian distribution. By using these probabilistic characteristics, the stochastic fluctuations in the soil stress can be rapidly calculated without using Monte-Carlo simulations. Moreover, a new method based on derivatives of degradation functions and on inverse functions of the degradation functions is also suggested to calculate the mean degradation index. These methods significantly decrease the required computational effort, thus overcoming a critical drawback of existing methods. Case studies demonstrate that the dimensions of substructures significantly affect the evolution of the dynamic response, suggesting that the evolution of the dynamic response should be considered in the design process to secure the serviceability of offshore wind turbines and substructures.
Selective Maintenance under Uncertainty
Authors: Liu Yu , Jiang Tao
Published: March 2024

In this paper, a new multi-objective robust selective maintenance model is formulated by considering the uncertainties produced by imperfect inspections. The resulting optimization problem aims at maximizing the expectation of the probability of a repaired system completing a mission and simultaneously minimizing its variance. A multi-objective particle swarm optimization algorithm in introduced to identify the Pareto front, which offer a set of non-dominated selective maintenance strategies. An illustrative example shows that the selective maintenance strategy with the maximum expectation of the probability of a repaired system completing a mission may not be desirable as it usually possesses a huge variance. Several comparative studies are also conducted to examine the effect of observation accuracy and maintenance budget on the results. It concludes that the proposed approach can effectively improve the robustness of the probability of a repaired system completing a mission.
Probabilistic Fatigue Life Prognosis for Steel Railway Bridges after Local Inspection and Repair
Authors: Lee Young-Joo
Published: March 2024

Steel railway bridges are exposed to repeated train loads which often cause fatigue failure. To guarantee the target fatigue life, bridge maintenance such as local inspection and repair should be properly provided based on accurate fatigue life prognosis, but it is a challenging task because there are various sources of uncertainty associated with bridges, train loads, environment, and maintenance work. For the optimal risk-based maintenance, it is thus essential to predict the probabilistic fatigue life of a steel railway bridge and update the life prognosis information based on the results of local inspection and repair. In this research, a probabilistic approach is proposed to estimate the fatigue failure risk of steel railway bridges and update the prior information of fatigue life prognosis after bridges are inspected and repaired. The proposed method is applied to a generic steel railway bridge, and the effects of local inspection and repair on the probabilistic fatigue life prognosis is discussed through parametric studies.
What are the effects of the reliability model uncertainties in the maintenance decisions?
Authors: Castanier Bruno , Guérin Fabrice , Saintis Laurent
Published: March 2024

Most of the works proposed for the design of reliability test plans are devoted to the guaranty of the reliability performance of a product but scarce of them tackles maintenance issues. On the other hand, classical maintenance optimization criteria rarely take into account the variability of the failure parameters due to lack of data, especially when the data collection in the operating phase is expensive. The objective of this paper is to highlight through a numerical experiment the impact of the test plan design defined here by the number of the products to be tested and the test duration on the performance of a classical condition-based maintenance (CBM) policy.
DETECTION OF LOCAL BONDING FAILURE DAMAGE BY DIGITAL IMAGE CORRELATION TECHNIQUE
Authors: Huh Y.-H.
Published: March 2024

Detectability of the bonding failure damage for GFRP(Glass-Fiber Reinforced Plastic) composite under the in-plane and outof-plane loads was investigated through the 3-dimensional DIC(Digital Image Correlation) strain and deflection measurement. The damage was introduced to the tensile specimen and trailing edge of 100 kW wind turbine blade which were subject to inplane and out-of-plane load, respectively. DIC measurement was performed over the tensile specimen surface and the blade surface, including the damage, with a 3D DIC system(ARAMIS 5M). The longitudinal and transverse strain profiles for the inplane tensile specimen were determined, and the deflection profile and deflection, longitudinal and chordwise strain profile for the wind turbine blade were obtained. From these measurements, it was found that the dissimilarities, like local concentration and gradient, in the directional strain profiles were corresponded to the presence of the damage. Therefore, the bonding failure damages in the tensile specimen and wind turbine blade could be detected from the deflection and directional strain  profile. Beside, in this study, sensitivity of these profiles to the applied loads was examined to determine an optimal load level for DIC measurement.
Condition monitoring using compressive measurement with variance considered machine algorithm
Authors: Jeon Jun Young , Lee Myung Jun , Park Gyuhae , Kang To , Han Soon Woo
Published: March 2024

In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and the improved damage detection capability. A built-in rotating system was used for demonstration. Data were then compressively sampled to obtain compressed measurement. For damage detection, Variance considered machine (VCM) algorithm was employed to classify failure modes of rotating systems. The experimental results showed that the proposed method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.
Hybrid Nanocomposite-Based QCM Sensor for Detecting DMMP
Authors: Lee Young-Jun , Kim Jong-Gyu , Ramesh Sivalingam , Kim Heung-Soo , Kim Joo-Hyung
Published: March 2024

Taking a hybrid nanocomposite of nitrogen doped multiwalled carbon nanotube/cobalt oxide nanoparticle/gold nanoparticle as a sensing material, we developed a quartz crystal microbalance-based nerve simulant sensor. Thesensor was simply fabricated by a drop casting of a mixture of the nanocomposite and ethanol on the top of gold electrode in a quartz crystal microbalance (QCM). Dimethyl methylphosphonate (DMMP) vapor as a targeted nervesimulant was generated by a bubbler system and modulated by two mass flow controllers. The frequency shifts taken from QCM was linearly responded to DMMP concentrations, ranging from 20 to 120 ppm. The adsorption rate of DMMP molecules was relatively high, while the desorption rate was low. It reveals a high sensitivity to DMMP vapor. To investigate the selectivity various VOCs including ethanol, water, toluene, methanol, and n-hexane were tested at 120 ppm of targeted VOCs concentration. Interestingly, the targeted VOCs other than DMMP were relatively low detected by the QCM sensor, implying remarkable selectivity. In this regards, the developed hybrid nanocomposite is a potential material to be used for sensing nerve simulants.
A Multivariate CUSUM Chart Handling Auto- and Cross-correlated Observations
Authors: Lee Kyu Young , Park Chuljin , Lee Mi Lim
Published: March 2024

Multivariate CUSUM charts have been widely used as statistical-process-control tools to detect out-of-control states of monitoring variables. Most of earlier studies regarding multivariate CUSUM charts assume that observations of monitoring variables are independent or correlated with a limited structure. In this paper, we suggest a multivariate CUSUM chart that can handle the observations auto- and cross-correlated. The chart estimates its control limitanalytically and captures small mean shifts faster when compared to existing charts.
Connected freight rail rolling stock: a modular approach integrating sensors, actors and cyber physical systems for operational advantages and condition based maintenance
Authors: Pfaff Raphael , Shahidi Parham , Enning Manfred
Published: March 2024

For decades, the technology of freight railcars has not changed significantly, mostly due to little or no incentive for significant investments in rolling stock. Taking into account the disruptive developments anticipated in automotive transportation, this approach appears no longer feasible, especially if regulatory agencies aim to reduce carbon dioxide emissions while maintaining economic growth. With the advent of telematics, on-board sensing and cloud-based analytics for control and condition based maintenance, high potential for efficiency improvements has become possible. Such technologies are de facto standards in automotive transport, which induces the need for implementation of similar technologies in rail transport as well. In addition to enabling efficiency gains, telematics, on-board sensing and cloud-based analytics also offer new means to approach pressing problems such as rail noise emission, train integrity and safety against derailment, while at the same time reducing maintenance cost and downtime. Furthermore, a connected wagon offers a seamless integration into current and future logistics systems, which are driven and controlled by the industrial Internet of Things to support the fourth industrial revolution. An important concept, introduced with theWagon 4.0, is standardized hardware, together with an open-source operating system based on prognostics and health management principles for predictive analytics. Thus, the Wagon 4.0 paves the way for new operations and maintenance concepts, user interfaces and value proposals. Additional economic advantages will be made possible from the self-organizing features of such vehicles, the ability to achieve mass customization and from a rise in efficiency in operation and maintenance. This paper describes the basis of such a system including the power supply, intra-train communications, sensing and cloud-based analytics. A study of use cases from railway operation illustrates the approach and highlights the opportunities of this novel system design. The paper concludes with a description of how the implantation enables the railcar operator to practice predictive maintenance and increase operational efficiency.
Detection of Localized Faults in Bearings using 2D Envelope Signal Analysis
Authors: Khan Sheraz Ali , Kim Jaeyoung , Kim Jong-Myon
Published: March 2024

Bearing faults are the leading cause of failure in induction motors and result in the longest downtime per failure in wind turbines. The accurate and timely detection of these faults is essential in avoiding unexpected shutdowns  and the consequent economic losses. State of the art bearing fault detection is mostly based on envelope analysis. It generally involves band pass filtering the raw signal, demodulating the filtered signal to construct the envelope and then the Fourier analysis of the envelope signal to determine the presence of peaks at characteristic defect frequencies and their sidebands. Recent work on envelope analysis is mostly concerned with improving the visibility of these defect frequencies in the envelope power spectrum by selecting an optimal band for filtering. This is done using techniques like sub-band analysis and spectral kurtosis, thereby making the process tedious and computationally complex. This paper presents a 2D imaging based approach to envelope signal analysis, which is simple yet highly effective. The envelope signal, when projected onto a two dimensional grayscale intensity space, shows a unique texture for each fault. This study shows that these unique textures can be used for automated detection of localized defects in bearings.
A Study on the Improvement of Reliability of High-Performance Hydraulic Servo Actuator
Authors: Lee Yong-Bum , Kim Tae-Seok
Published: March 2024

A tension-compression fatigue testing equipment is broadly used to secure the reliability of the components related to mechanics, automobiles, electronics and materials. This test equipment has the proper characteristics, varying the loads, displacements, and frequencies, for the acceleration life test of mechanical components. Also, this has used the hydraulic servo actuator for operation, which is required higher performance and durability than any other types of equipment. The functions and durability of the hydraulic servo actuator of the test equipment were backed up and have occurred the failures by overloaded, because of extending the operation life of the components due to the reliability improvement, increasing the accelerated load condition due to the extended operation environments, the life test time, and test frequency. This study analyzed the failure reasons for the hydraulic servo actuator of the tension-compression fatigue testing equipment requiring extended durability and introduced the examples improved on the reliability through the fundamental solutions.
Model-Based Prognostic Approach for Battery Variable Loading Conditions: Some Accuracy Improved
Authors: Mishra Madhav
Published: March 2024

Prognostics and Health management (PHM) using a proper condition-based maintenance (CBM) deployment is a worldwide-accepted strategy and has grown very popular in many industries and academia over the past decades. PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. Using this technology, one can estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions. This paper deals with the improvement of prognostic accuracy for battery discharge prediction and compare with previous results done by the other researchers. In this paper, physical models and measurement data were used in the prognostic development in such a way that the degradation behaviour of the battery could be modelled and simulated in order to predict the end-of-discharge (EoD). A particle filter turned out to be the method of choice in performing the state assessment and predicting the future degradation.
Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis
Authors: Zheng Bo , Huang Hong-Zhong , Zhou Jie , Li Yan-Feng
Published: March 2024

To improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the graph partition based on dimensionless similarity (GPDS) method is proposed to overcome the influence of attributes with different dimensions. The algorithm is named graph partition based on dimensionless similarity. Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Meanwhile, a weighted relative error is proposed to measure quantitatively the distribution change of original data after being compressed. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly
Usage of Accelerated Test Data for Predicting Remaining Useful Life at Field Operating Conditions
Authors: An Dawn , Choi Joo-Ho , Kim Nam Ho
Published: March 2024

Prognostics predicts future damage/degradation and the remaining useful life of in-service systems based on damage data obtained during previous usage. General prognostics methods are physic-based approaches when physical models and loading conditions are available and data-driven approaches when only the damage data are available. The damage data are of great importance regardless of prognostics methods used, but it is very expensive to obtain data from in-service systems because of time and cost. Instead, companies frequently perform accelerated tests under much more severe operating conditions for the purpose of design. This paper presents a method of utilizing accelerated degradation data for the purpose of prognostics. As an example, crack growth data are synthetically generated under over-loaded conditions, which are utilized to predict damage growth and remaining useful life at field operating conditions. Four different scenarios are considered based on the availability of a physical model and field loading conditions. Using accelerated test data increases prediction accuracy in the early stages of physics-based prognostics and also compensates for the insufficient data problem in data-driven prognostics.
Wavelet-like CNN Structure for Time-Series Data Classification
Authors: Park Seungtae , Jeong Haedong , Min Hyungcheol , Lee Seungchul
Published: March 2024

Vibration is one of the richest information in manufacturing field. Due to its cheap acquisition, vibration has become “big data” in manufacturing fields. Recently, deep learning models shows state-of-art performance on analyzing big data due to its sophisticated structure. Traditional models for a machinery monitoring system has been highly dependent on features selected by humans. Besides, its representational power fails as data gets complicatedly distributed. On the other hand, deep learning models automatically select highly abstracted features while optimization process and its representational power overcomes traditional models. However, its applicability in PHM field has been investigated mainly based on image data. This paper introduces a CNN model for a ‘vibration data’ which is the richest data in manufacturing field. We integrate ‘residual fitting’ mechanism into the CNN structure. As a result, the architecture combines signal re-construction and classification procedures into a single model. Validation results based on the rotor vibration data suggest our model outperforms any other off-the-shelf feature-based models and the deep learning models recently proposed in PHM field.
Imbalanced Classification for Fault Detection in Monitored Critical Infrastructures
Authors: Li Yan-Fu
Published: March 2024

Safety and reliability are among the most crucial factors for the critical infrastructures (CIs). For this reason, they are typically closely monitored and large amounts of data have been collected. Due to their importance, CIs are designed to be highly reliable such that fault cases are rare in the Bigdata set. This renders the fault detection an imbalanced binary classification task. In this work, we developed accurate data mining classifier to tackle this problem. The imbalance ratio of the data can be more than 200.
A Health Monitoring Method for Wind Power Generators with Hidden Markov and Probabilistic Principal Components Analysis Models
Authors: Sasaki Riku , Takeishi Naoya , Yairi Takehisa , Hori Koichi , Ide Kazunari , Kubo Hiroyoshi
Published: March 2024

In this work, we propose a data-driven health monitoring method for wind power generators, which learns an empirical model from the time-series sensor data and detects irregularities or faults in the turbines and blades. Our main objective is to predict any symptoms of faults as early as possible before the generators fall into malfunction. The data obtained from the wind power generators are strongly correlated multidimensional time-series with multiple states. In this study, we take such features into account and develop the probabilistic model for them, namely, hidden Markov and probabilistic principal component analysis. Once the model is learned with the data that contain no faulty events, it can be used to detect faults in new data by comparing the original sensor values and reconstructed ones. In this research, we apply this method to synthetic data and real-world wind turbine data and show the results of experiments to confirm the availability of the proposed method.
A Study On The Application Of AAKR Based Early Warning System For ICE
Authors: Min Hyungcheol , Kim Heesoo , Sohn Seokman , Bae Yongchae
Published: March 2024

Internal Combustion Engine (ICE) is a major type of power generating plant in the islands area and Emergency Diesel Generator (EDG) for nuclear power plants. All the electricity workload in the islands area are supported bydiesel engine but due to harsh environmental conditions and lack of manpower, diesel engines in islands are not managed properly. The components of diesel engines are mostly affected by electrical/mechanical problems. Therefore, engines installed in islands area are vulnerable to unexpected failure and replaced earlier than the expected product life. In this paper, we suggest an early warning algorithm that detects machine breakdown before it happens to prevent unexpected machine failure. By applying this algorithm, we expect a life extension of the diesel engine through proactive and efficient maintenance. AAKR (Auto Associative Kernel Regression) is a multivariate state signal estimation model, which is a core algorithm of the early warning system. This algorithm compares saved normal state data with acquired present state data and calculates the weight to create estimation signals.In this research, we analyzed 2 diesel engine operation data and confirmed that the algorithm works properly. In the future, based on this research result, we plan to build an IoT (Internet of Things) based diesel engine early warning system.
State Estimation of Degradation Process Subject to Random Change of Mode
Authors: Lin Yan-Hui , Li Xiao-Yang , Kang Rui
Published: March 2024

Due to changes of the surrounding environment, the dynamic of one degradation process may change at random time and it follows different modes before and after change occurs. For solving on-line degradation state estimation problems subject to random change of mode, a novel state estimation method is proposed in this paper based on the degradation models and related monitored data. The proposed method employs (1) sequential probability ratio test based on log-likelihood ratio to detect the unknown change time of degradation mode, and (2) particle filtering to estimate the degradation states given observations and also to evaluate the decision functions of the sequential probability ratio test. A case study is presented to illustrate the effectiveness of the proposed method.
A Study on Reliability Analysis and Life time Improvement Method by Field Failure of Vibro Hammer
Authors: Lee Hankwon , Go Youngho , Choi Joo-Ho
Published: March 2024

Vibro hammer is a construction equipment that uses vibration to drive the foundation piles and H-beams underground. On the work site, vibration is generated and tilting, swinging and jaw opening/closing is made by using the hydraulic pressure and flow rate supplied by the excavator. Since the equipment is usually used overseas or in remote location, the unanticipated failure may lead to excessive down time cost and customer complaint, which is the reason that high durability is demanded. In order to achieve longer life, on-site failure cases have been investigated that have occurred from January 2014 to December 2015. Statistical analysis is carried out to estimate the associated reliability parameters of each module in the equipment, from which the suggestions are made for improving the system reliability.
A Proposal for Applying Adaptive Wireless Communication System for Smart Factory
Authors: Sakakura Takashi
Published: March 2024

Activities to deploying devices that connect Internet, said Internet of Things in the industrial domain are accelerating to improve efficiency, prevent down time led by organizations such as Industry 4.0 and Industrial InternetConsortium. Moreover a trend in industrial domain that manufactures have to provide various and small amount of products let manufactures reorganize their manufacturing facilities frequently. In often the case, the most costlyportion of the reorganization is re-cabling cost. Basing on this background, wireless communications are highly desired especially devices that can operate without power line. In this study, we propose a wireless communicationsystem that configure properties of the system such as carrier wave frequency, bandwidth, modulation autonomously depending on application requirements and circumstance of radio wave propagation.
Bearing Race Faults Classification using Simulation-generated Training Data and Feature Free Methods
Authors: Sobie Cameron , Freitas Carina
Published: March 2024

Rotating machinery is central to transportation and power generation, and maximizing its uptime while minimizing unplanned maintenance is important from safety and economic perspectives. Roller bearings are ubiquitouscomponents in such machinery and are very often the cause of failures; thus, condition monitoring of roller bearings is a topic of key interest to many industries. Historically accomplished using human expertise and experience toestimate the condition of a machine from a limited set of signals and curated statistical features, machine learning has become an effective tool in bearing fault identification with modern computing and algorithmic advances. In this regard, two major challenges exist: first, the availability of inservice data from bearings containing faults is often rare or difficult to obtain, and moreover, for machines being deployed to the field for the first time, no such data exists. Secondly, the extracted statistical measures (features) must be chosen to maximize the classifier accuracy (or another metric), and their selection of these to maximize accuracy can be a challenging task. We directly address these challenges with two separate approaches. With validation against four experimental datasets, we show that in the absence of recorded in-service data, machine learning algorithms trained using simulated bearing vibration signals (i.e. simulation-driven machine learning) can classify bearing race faults with greater accuracy than those trained using data collected from other machines. Next, we propose convolutional neural networks and nearest-neighbor dynamic time warping (NNDTW) as statistical feature-free methods to detect bearing race faults using a signal processing pipeline based on angle synchronous averaging. We show that these methods offer superior accuracy from the simulation-driven perspective and can predict an inservice wind turbine fault over one month before failure.
Development of an Effective Strategy for Prognostic Monitoring of a Large Centrifugal Air Compressor in an Automotive Plant
Authors: Kim Hyunsu , Kim Jay H. , Song Won Joon
Published: March 2024

Prognostic monitoring of health condition of a large centrifugal air compressor that supplies compressed air in an automotive plant is crucial because its failure will seriously impair operation of the entire plant. It was desired todevelop an effective prognostic maintenance methodology of air compressors after the failure of an air compressor in one of major automotive companies in US, which brought a highly undesirable situation to the manufacturing line of the plant. In this work, the shaft motion of the compressor measured at transient and steady-state conditions were used to develop techniques and a strategy for effective prognostic monitoring. The pseudo frequency response function (FRF) obtained from the Campbell diagram and directional Power Spectrum (dPS) were new techniques employed to develop the prognostic health monitoring strategy. The analytic wavelet transform (AWT) is adopted to monitor temporal change of the system characteristics during the start-up period. In addition, AWT was utilized to monitor the steady state condition.
Application of clustering algorithms and classification methods for identifying stench data of automobiles
Authors: Lee Chinuk , Mun Byeongmin , Lee Junseop , Kim Hyoseok , Lee Hansin , Bae Sukjoo
Published: March 2024

There has been steady increase in consumer’s complaint about the affective quality for automobiles such as the stench inside an automobile. In order to improve the affective quality of smell for consumer, it is crucial to cluster or classify the types of smell at first. We apply several clustering algorithms, such as hierarchical algorithm and K-means algorithm, and classification models, such as decision tree and artificial neural network, and support vector machine, to 26 indices provided from ‘A’ automobile company. After determining optimal number of clusters, we apply three classification methods and compare their performance in terms of computational time and accuracy.
Independent Component Analysis Method Based on Genetic Algorithm in Compound Fault Separation
Authors: Hao Yansong , Song Liuyang , Li Jingle , Xie Xin , Wang Huaqing
Published: March 2024

Compound faults often exist in roller bearing, which increase the difficulty for the fault diagnosis. In order to extract the characteristics of compound fault signals, independent component analysis (ICA) method was used toseparate fault signals. However, the selection of initial weight about ICA will affect the number of iterations and astringency. Therefore, a novel ICA method based on improved genetic algorithm was proposed. The kurtosis ofsignal was used as the optimization function, and then genetic algorithm was applied to find the separation matrix according to the maximum of the kurtosis. This method avoids the problem of redundant iteration and convergence, which is caused by randomness of initial weight vector. Finally, the proposed method was used to separate source signal from the mixed signals and achieve the roller bearings fault identification and separation. The results show that the proposed method is superior to traditional ICA method, and the compound fault can be separated based on the proposed method.
A Study on Monitoring and Fault Diagnosis of Fused Deposition Modeling Process Based on Data-driven Approach
Authors: Kim Jung Sub , Lee Sang Won
Published: March 2024

Additive manufacturing (AM) is a layer by layer manufacturing process that can fabricate a threedimensional part directly from a computer aided design (CAD) model. In particular, a fused deposition modeling (FDM) process is the most widely used AM technique for fabrication of thermoplastic parts. They can be used for making functional prototypes with advantages of low cost, minimal wastage and ease of material change. Despite its recent popularity, FDM still faces many technical challenges for insufficiency of process reliability and controllability and product quality. Therefore, to overcome such disadvantages, the monitoring and fault diagnosis on FDM process is of much significance. In this study, the monitoring on quality of parts and components fabricated by the FDM process is conducted by analyzing mass data which are obtained from various sensors such as accelerometers, acoustic emission sensors and temperature sensors. After extracting critical features from the measured process signals, they are related with quality evaluation indices in the model by using data-driven modeling techniques. Finally, the developed model is validated via a series of experiments.
Fault Detection of Tripod Shaft using Dual Strain Sensor
Authors: Lee Sunghyun , Jeon Insu , Kim Do Sik , Baek Dong-Cheon
Published: March 2024

Tripod shafts used in high-speed trains transmit power of motor reduction unit to axle reduction unit. Since the load applied to the reduction units is very large during operation, it is designed as the shaft with a fuse to protect gears of reduction units. Failure of the fuse can lead to accidents and economic loss such as train service interruption and repair problem in accident site. However, it is difficult to inspect the fuse covered by the tripod shaft housing. So, it is necessary to detect the fuse of the tripod shaft in advance. In this study, torsion fatigue tests considering failure mode were carried out and strain data were collected by strain sensors at around weak point of that. Thresholds for the damage detection of the tripod shaft fuse were defined by feature extraction from sensor data. We evaluated the detection performance of the thresholds.
Architecture for Online Prognostics for Pumps and Valves in Nuclear Power Plants
Authors: Kim Yang-seok , Lee Byoung-oh , Min Ji-ho , Kim Dae-woong , Park And Chi-yong
Published: March 2024

The health of the safety-related equipment in a nuclear power plant is crucial to safe and reliable operation of the plant. To achieve this, a number of monitoring systems have been equipped and strict guidelines and procedures applied. Recently, several real-time diagnostics and predictive diagnostic concepts have been proposed. This paper presents an architecture for online prognostics to monitor, diagnose and prognose feed water pumps and pneumatic control valves in nuclear power plants. We describe the architecture from the following points: system design and process. Diagnostic method with positioner model for a double-acting pneumatic control valve is also presented. A pilot system, developed based on the proposed architecture and diagnostic method, is applied to the pneumatic control valve in a nuclear power plant. The result shows that the proposed architecture can be applied to online and real-time diagnostics and prognostics.
Parameter Estimation Using Particle Filter for Induction Machines under Inter-Turn Fault
Authors: Nguyen Viethung , Wang Danwei , Seshadrinath Jeevanand , Ukil Abhisek , Vaiyapuri Viswanathan , Nadarajan Sivakumar
Published: March 2024

Parameter estimation has found its applications in various domains. In this paper, it is applied for fault severity estimation. A method, using particle filter approach, for estimating unknown constant fault parameters in stator winding inter-turn short is proposed. These parameters are insulation resistance and percentage of shorted turns. The method uses only measurements of stator voltages and currents. In order to effectively estimate the parameters, firstly, a sequence components-based approach is applied to derive an equality constraint on the magnitude of a state variable, which works as additional information for estimation algorithm based on state-state model. Secondly, the variance reduction technique is applied to increase the accuracy of the method.
System Health Monitoring of Wind Turbines Using SCADA Data and Gaussian Mixture Models
Authors: Yasuda Akihisa , Ogata Jun , Murakawa Masahiro , Morikawa Hiroyuki , Iida Makoto
Published: March 2024

Wind turbines are the major driving force to produce renewable energy, but there is a strong need to reduce the costs of operation and maintenance. To detect anomalies of wind turbines, this paper proposes a method which uses data collected by the Supervisory Control And Data Acquisition (SCADA) system and is based upon building the normal behavior model of wind turbines. This is achieved by using supervised data and Gaussian mixture models with filtering SCADA data from the macroscopic point of view. The method is validated with SCADA data collected from actual 2-MW wind turbines. The result shows the potential of detecting anomalies and the effectiveness of filteringconditions for building the model.
A Self-Organization Strategy for Unmanned Autonomous Systems
Authors: Lee Benjamin , Oh Sehwan , Balchanos Michael , Vachtsevanos George
Published: March 2024

Complex systems are constructed from multiple subsystemsand components with each serving incremental tasks, wherethe “emergent” system behavior cannot be deduced from thebehaviors of the individual parts. The key requirement ofcomplex systems is the ability to compensate for unforeseenand extreme disturbances, so it is important to design acontrol method that ensures acceptable level of systemresilience throughout its operation. Therefore, detailed andaccurate knowledge of system behaviors is paramount for thedesign of complex system control strategies. This paperpresents a self-organizing control strategy that incorporatesboth situational awareness and failure impact compensationfor a resilient unmanned autonomous system.
Resilient-based Control Reconfiguration of Autonomous Systems
Authors: Oh Sehwan , Lee Benjamin , Balchanos Michael , Mavris Dimitri , Vachtsevanos George
Published: March 2024

This paper introduces a design methodology for resilientbasedcontrol reconfiguration of Unmanned AutonomousSystems (UAS) when extreme disturbances, such as a largelygrowing fault or component failure mode occur. It isdocumented that more than 40% of Class air mishaps areattributed to Unmanned Aerial Vehicles (UAVs). There is anurgent need to improve the operational integrity, resilienceand reliability of such critical assets. An optimal controlapproach with Differential Dynamic Programming (DDP)and Model Predictive Control (MPC) is introduced in thispaper as a means for control authority redistribution andreconfiguration; therefore, the system continues performingits mission while compensating for the impact of the extremedisturbances. Prognostic knowledge is considered in aquadratic cost function of the optimal control problem as asoft constraint. A trade-off parameter is introduced betweenthe prognostic constraint and the terminal cost. Anautonomous ground operable under-actuated hovercraft isused to demonstrate the efficacy of the proposedreconfiguration strategy, and it is extendable to other cyberphysical systems.
Bearing Fault Diagnosis using Singular Spectrum Analysis-Based Envelope Detection
Authors: Zhang Guicai , Li Yun , Li Changle
Published: March 2024

Rolling element bearings are critical components in rotating machinery and it is important to monitor their health and detect their faults in early stage during their operations. The vibration energy generated by the faults in rolling element bearings is usually small comparing to that of other rotating components such as rotors/shafts and gears in mechanical systems. Envelope analysis is a widely used method in bearing fault diagnosis. The major challenge in envelope analysis is the identification of resonance frequency band for narrow-band filtering before applying envelope operation. In this paper, a method combining singular spectrum analysis (SSA) and envelope analysis is proposed for diagnosing the rolling element bearing faults. The SSA is utilized to decompose the bearing vibration signal into a set of eigentriples (principle components), and then a subset of the eigentriples that encompass the dominant variation in the original signal is selected for signal reconstruction. Envelope analysis is then applied to the reconstructed signal to extract the modulation information that caused by the bearing faults. The proposed SSA-based envelope analysis is applied to the data sets of the bearing data center at Case Western Reserve University (CWRU), and the results are compared with that of the widely used Kurtogram-based envelope analysis. The results show that the SSA-based envelope analysis is more effective than the Kurtogrambased envelope analysis in bearing fault diagnosis.