Faulty Data Research Articles

Conditional monitoring and fault detection of wind turbines based on Kolmogorov–Smirnov non-parametric test

This research presents a new method for conditional monitoring based on the wind turbine power curve. The Kolmogorov-Smirnov (K-S) distribution test is employed in the assessment of turbine data and the detection of abnormality (faults) in wind turbines. The process begins with anomaly detection and filtration of faulty SCADA data by a quantile-based filtration approach. Suitable data comprising wind speed, air density, ambient temperature, and pitch angle are utilized in the development of wind turbine power curve models that represents actualities within wind farms. The radial basis function (RBF), multi-layer Perceptron (MLP), and gradient boosting (GBR) methods utilized for model development are compared for predictive accuracy using Mariano-Preve test. The null hypothesis assumes equal predictive ability (EPA); if rejected, an algorithm compares the coefficients of correlation of the models and selects the closest to one (unity). The most accurate model is utilized for the creation of a bin-wise distribution from past data, and bin-wise confidence levels from the plot of wind speed and output power. Cochran’s method was utilized to validate the minimum sample size that will possess a sampling distribution similar to that of the population, and a fault is detected if there is a reasonable difference between the sample distribution and population distribution. The K-S test, having a null hypothesis of equivalent distributions, signals a fault if the null hypothesis is rejected. Two wind turbine SCADA datasets associated with two fault events are used for the assessment of our method. The results indicate that our method effectively discovers abnormalities in power output relating to increased bearing temperature and reduced generator rpm, thereby aiding in the detection of faults long before they occur.

Research and implementation of fault data recovery method for dry-type transformer temperature control sensor based on ISSA-LSTM algorithm

This study proposes a temperature control sensor fault data recovery model based on the improved Sparrow Search Algorithm (ISSA) optimizing Long Short-Term Memory (LSTM) to address erroneous data output caused by temperature monitoring sensor failures in dry-type transformers. The model aims to recover faulty sensor data in dynamic processes. It enhances the algorithm by initializing the population using Tent chaotic mapping, employing t-distribution and differential variation perturbations, and introducing a dynamic step size factor. Simulation experiments evaluated the performance of ISSA-LSTM, SSA-LSTM, PSO-LSTM, GA-LSTM, and ACSA-LSTM algorithms. The ISSA-LSTM model outperforms the worst PSO-LSTM model by 82.6%, 82.1%, and 1.8% in terms of MAE, RMSE and R2 values, respectively. Field experiments confirmed the algorithm's effectiveness in recovering data from faulty sensors and different fault types, improving the accuracy and stability of temperature control sensor fault data recovery in dry-type transformers.

Diesel engine fault diagnosis for multiple industrial scenarios based on transfer learning

Fault diagnosis based on data-driven intelligence has recently attracted extensive interest owing to the rapid development of big data and deep-learning algorithms. However, when the amount of faulty data is limited, deep learning training is prone to overfitting. When the application scenario is changed, the generalization ability of the trained network is affected. In this study, a fault diagnosis architecture based on deep transfer learning is proposed to work with limited data and transfer between multiple scenarios. A wide convolution kernel convolutional long short-term memory neural network (WCL) was used to improve the feature extraction ability of fault data from a diesel engine with a low signal-to-noise ratio. A multiple transfer learning scheme based on WCL was further adopted to transfer the well-trained diagnostic knowledge of large-scale labeled source domain data to the target domain with limited samples. In addition, for diesel engines for various purposes, the knowledge transferability between different scenarios was studied. The algorithm evaluates the transfer performance of four different domains when the sample is insufficient, including the cross-fault type, cross-equipment type, cross-fault degree, and cross-working conditions. The results show the proposed method is proven with high noise immunity improves the accuracy of small sample cross-domain diagnosis and provides an optimal transfer scheme suitable for diesel engine fault signals.

Hurdles experienced by LIS authors from Pakistan during the operability or implementation of selected research methodologies: A qualitative exploration

Use of appropriate research methodology among authors is essential to produce high quality research. Practical operability or smooth implementation of research methodology and methods must be maintained because lapses in the implementation may hamper research and quality. Hurdles experienced by authors in the operability of research design may negatively impact the conclusions. Researchers need to be aware about the issues arising during the process of research and they must adopt certain steps to avoid such issues. The current study explored the hurdles, negative impacts and relevant measure to streamline the operability of research process. The current empirical research used qualitative design. Interview method was used for exploring author’s views. Pakistani LIS authors were selected for interview from those who have published more than one article during the period 2001 and 2016. Majority of the respondents experienced hurdles in the operability of selected research methodology. Few respondents faced problems because they selected inappropriate research method for the research problem, questions or phenomenon. Selection of irrelevant population and faulty sampling also create disturbances in the smooth execution of research process. Use of faulty data collection tool resulted in the collection of irrelevant, meaningless, neutral, or over rated data. Lack of access to data and its inventory nature and complexity of data analysis procedure also affected smooth operability. Hurdles created negative impact on the quality of research and compromised the accuracy, reliability and validity of results. Multiple strategies were proposed by authors to avoid such hurdles. The current study is an effort to help authors gain an understanding for better implementation and subsequently to make more informed decisions for selection of research methodology and methods.

Artificial Intelligence: Increasing Business Profits at the Cost of Consumer Privacy

Artificial Intelligence may be the most significant technological breakthrough since the development of computers. The promise of greater efficiency and effectiveness is very appealing, especially as that translates into more profitable businesses. The potential applications appear almost endless, crossing every industry and every form of business activity. The potential results cannot even be estimated.
 Artificial Intelligence can assist in detecting misinformation, but it can also assist in spreading misinformation when faulty algorithms collect incorrect data. An example of this occurs when customer demographic data mixes information from consumers with the same names. Persons who frequently relocate may also be more likely to have faulty consumer data collected by business research companies. However, in our rush to be the first and the best, we must ask ourselves how much of our privacy we are willing to give up? In this paper, the authors examine this fascinating emerging technology with a preview of the technology’s business potential.

A robust neural network model for fault detection in the presence of mislabelled data

AbstractSeveral data‐driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data‐based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low‐quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low‐quality data issues which are crucial in the data‐driven modelling of processing systems.

Fault prediction of gyrotron system on test bench using a deep learning algorithm

The gyrotron system is an essential component of the Electron Cyclotron Resonance Heating (ECRH) system, generating high-power millimeter waves that can be utilized for plasma heating and current drive in magnetically confined nuclear fusion. During high-power long-pulse operation, the gyrotron is prone to faults such as arcing, mode jump, and overcurrent, leading to interruptions in continuous system output. These faults not only endanger the safety of the gyrotron but also reduce the overall efficiency and performance of the system. Addressing this issue, we have employed deep learning methods to predict faults that may occur during the gyrotron’s long pulse operation. Using experimental data collected from the megawatt-level continuous wave (CW) gyrotron test bench since 2019, we constructed a dataset comprising 52,772 data points, including 23,241 instances of faulty data. We trained a model based on the Transformer encoder architecture and compared its performance against classical CNN and LSTM models. The results demonstrated that the Transformer model exhibited superior performance in this context, achieving an AUC value of 0.869. The True Positive Rate (TPR) and False Positive Rate (FPR) reached 85.2% and 18.2%, respectively. Subsequent offline testing verified that the trained model could successfully predict faults in advance with second-level accuracy, laying the groundwork for future gyrotron operation management. In conclusion, deep learning has demonstrated its potential application in the prediction of operational faults in the gyrotron system.

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work. To solve it, a joint reparameterization feature pyramid network (JRFPN) is proposed. Firstly, three different reparameterization tricks are designed to reform temporal information and exchange cross-temporal information, to alleviate the damage of long-term dependency. Secondly, a joint condition monitoring framework is designed, aiming to suppress feature confounding between poor-quality data and faulty data. The auxiliary task is trained to extract the degradation trend. The main task fights against feature confounding and dynamically delineates the failure threshold. The degradation trend and failure threshold decisions are corrected for each other to make the final joint state inference. Besides, considering the different quality of the monitoring variables, a channel weighting mechanism is designed to strengthen the ability of JRFPN. The measured data proved that JRFPN is more effective than other methods.

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

The Chicago Array of Things (AoT) is a robust dataset taken from over 100 nodes over four years. Each node contains over a dozen sensors. The array contains a series of Internet of Things (IoT) devices with multiple heterogeneous sensors connected to a processing and storage backbone to collect data from across Chicago, IL, USA. The data collected include meteorological data such as temperature, humidity, and heat, as well as chemical data like CO2 concentration, PM2.5, and light intensity. The AoT sensor network is one of the largest open IoT systems available for researchers to utilize its data. Anomaly detection (AD) in IoT and sensor networks is an important tool to ensure that the ever-growing IoT ecosystem is protected from faulty data and sensors, as well as from attacking threats. Interestingly, an in-depth analysis of the Chicago AoT for anomaly detection is rare. Here, we study the viability of the Chicago AoT dataset to be used in anomaly detection by utilizing clustering techniques. We utilized K-Means, DBSCAN, and Hierarchical DBSCAN (H-DBSCAN) to determine the viability of labeling an unlabeled dataset at the sensor level. The results show that the clustering algorithm best suited for this task varies based on the density of the anomalous readings and the variability of the data points being clustered; however, at the sensor level, the K-Means algorithm, though simple, is better suited for the task of determining specific, at-a-glance anomalies than the more complex DBSCAN and HDBSCAN algorithms, though it comes with drawbacks.

HVAC early fault detection using a fuzzy logic based approach

The need for improving the energy efficiency of existing buildings has driven to the implementation of building energy management systems (BEMS) that can help facilities manager to discover and identify problems that may cause energy wastage or affect to occupants’ comfort. Modern data-driven fault detection and diagnosis (FDD) make use of the data collected by the building BEMS to provide high accuracy in the revelation of heating, ventilation and air-conditioning (HVAC) system faults. However, these methods need a large amount of faulty data samples during the training, which is an uncommon situation in the real world. The main focus of this paper is to present a methodology to detect faults when the number of faulty samples is low. For this purpose, a regression-based methodology based on an adaptative neuro-fuzzy inference system (ANFIS) chiller model is developed using the data collected from a real use case. The model presents good results, that can be used for benchmarking the machine operation and detect the abnormal operation states.

Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on One-Class Support Vector Machines

This work proposes a fault prognosis methodology to predict the main bearing fault several months in advance and let turbine operators plan ahead. Reducing downtime is of paramount importance in wind energy industry to address its energy loss impact. The main advantages of the proposed methodology are the following ones. It is an unsupervised approach, thus it does not require faulty data to be trained; ii) it is based only on exogenous data and one representative temperature close to the subsystem to diagnose, thus avoiding data contamination; iii) it accomplishes the prognosis (various months in advance) of the main bearing fault; and iv) the validity and performance of the established methodology is demonstrated on a real underproduction wind turbine.

Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts.

Understanding the input and output of data wrangling scripts is crucial for various tasks like debugging code and onboarding new data. However, existing research on script understanding primarily focuses on revealing the process of data transformations, lacking the ability to analyze the potential scope, i.e., the space of script inputs and outputs. Meanwhile, constructing input/output space during script analysis is challenging, as the wrangling scripts could be semantically complex and diverse, and the association between different data objects is intricate. To facilitate data workers in understanding the input and output space of wrangling scripts, we summarize ten types of constraints to express table space and build a mapping between data transformations and these constraints to guide the construction of the input/output for individual transformations. Then, we propose a constraint generation model for integrating table constraints across multiple transformations. Based on the model, we develop Ferry, an interactive system that extracts and visualizes the data constraints describing the input and output space of data wrangling scripts, thereby enabling users to grasp the high-level semantics of complex scripts and locate the origins of faulty data transformations. Besides, Ferry provides example input and output data to assist users in interpreting the extracted constraints and checking and resolving the conflicts between these constraints and any uploaded dataset. Ferry's effectiveness and usability are evaluated through two usage scenarios and two case studies, including understanding, debugging, and checking both single and multiple scripts, with and without executable data. Furthermore, an illustrative application is presented to demonstrate Ferry's flexibility.

Fault Diagnosis of Complex Industrial Systems Based on Multi-Granularity Dictionary Learning and Its Application

Nowadays, the intelligent fault diagnosis problem of modern industrial systems has received increasing attention. However, with the increasing scale of industrial systems, the same category of faulty data often contains multiple working conditions, which leads to the multi-granularity of faulty data and the increasing complexity of data distribution. The possible existence of fine-granularity inter-class similarities in multi-granularity data can interfere with coarse-granularity recognition tasks, which brings challenges to data-driven fault diagnosis tasks. To solve the above issue, a fault diagnosis method based on multi-granularity dictionary learning is proposed in this paper. Specifically, the proposed method integrates prior knowledge into the dictionary learning framework by introducing the concept of “centroid atom”, which embeds the multi-granularity structure in the dictionary and improves the discrimination of dictionary atoms. In order to validate the proposed approach, experiments are conducted on a numerical simulation case and a zinc smelting roasting process. Compared with some state-of-the-art methods, the proposed approach performs better in the classification accuracy which shows that it can make full use of the knowledge provided by the multi-granularity structure while it is difficult to be modeled by the other methods. Therefore, it can be applied to fault diagnosis tasks more precisely and efficiently. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the fact that industrial faults often occur in different modes, and different faults may occur in the same mode, the fault data often has multi-granularity characteristics, this paper proposes a multi-granularity dictionary learning method for complex industrial fault diagnosis. The proposed method can learn the multi-granularity structure in the data, and the obtained model contains both coarse-grained and fine-grained information, which effectively improves the fault diagnosis accuracy. Intensive experimental results show that the proposed method performs better than some state-of-the-art methods, it can make full use of the knowledge provided by the multi-granularity structure, while it is difficult to be modeled by others. Above all, it is verified as suitable for fault diagnosis of real industrial systems.

Kernel principal component analysis fault diagnosis method based on improving Golden Jackal optimization algorithm

Aiming at the shortcomings of the Golden Jackal optimization algorithm, such as low convergence accuracy and easy falling into the optimal local solution, an improved Golden Jackal optimization algorithm was proposed. First, sine and piecewise linear (SPM) chaotic mapping was introduced to increase the population number to achieve the purpose of initial population diversity. The self-adaptive weight and sine–cosine algorithm improved the position update formula of the Golden Jackal optimization algorithm, so the global search ability of the golden jackal algorithm is improved, and avoid the algorithm that fell into local optimality. Second, simulation experiments with eight standard test functions are performed to prove that the algorithm has excellent optimization ability. The improved Golden Jackal optimization algorithm was applied to optimize the kernel parameters of hybrid kernel principal component analysis. A fault diagnosis model is proposed to improve the golden jackal algorithm to optimize the kernel principal component analysis. Finally, the proposed method is used to fault diagnosis in the hot strip mill process. According to the study of simulation results, the faulty data can be identified effectively by this method, the accuracy is up to 100%, and the fault false alarm rate is greatly reduced.

A novel ensemble learning approach for fault detection of sensor data in cyber-physical system

Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications.

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

&lt;p&gt;The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.&lt;/p&gt;

Interpretable fusion methodology of health indices with an application to industrial turbine cavitation condition monitoring.

Using health indices (HIs) to characterize machine conditions is greatly helpful to prevent machine failures and their subsequent catastrophe. Fusion and interpretation of the main contributions of HIs to machine condition monitoring are still challenging. In this paper, an interpretable fusion methodology of HIs is proposed for machine condition monitoring. The proposed methodology begins with elements of statistical learning for classification, following by an essence of how HIs are fused with their associated linear weights to realize machine condition monitoring. One main contribution of this paper gives a theoretical justification for positive and negative weights of the proposed fusion methodology for understanding their importance for machine condition monitoring and making the proposed methodology physically interpretable. In order to be suitable for two practical situations, in which whether faulty data are available or not, two solutions including an offline solution with healthy and faulty datasets and an online solution with only available healthy datasets are suggested to estimate interpretable weights of the proposed methodology. Finally, industrial turbine cavitation status data collected from our group are used to verify the proposed methodology and show its superiority to two existing popular machine fault diagnosis methods. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

Selection of relevant features to detect and diagnose single and multiple simultaneous soft faults in air-source heat pumps

Soft faults in air-source heat pump systems can cause various issues, such as decreased performance, increased energy consumption, and long-term damage. This paper presents a comprehensive modeling approach of air-source heat pumps that considers individual and simultaneous soft faults validated against empirical experiments from the literature, a perspective limited in previous research. The methodology is based on the simulation of fault-free and faulty system performance utilizing heat pump modeling software. The results are a selection of variables and their trends to select the appropriate features to detect and diagnose single and double soft faults. The model also computes the impacts of these combined faults on COP, revealing significant reductions. Specifically, combinations of overcharge with condenser fouling and undercharge with compressor valve leakages induce COP reductions ranging from 17% to 38%, depending on operating conditions. The model can be used to develop faulty heat pump data, which can be adapted to other system typologies and conditions and used as input for data-driven Fault Detection and Diagnosis (FDD) methodologies.