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The data-based fault detection and isolation (DBFDI) process becomes more potentially challenging if the faulty component of the system causes partial loss of data. In this paper, we present an iterative approach to DBFDI that is capable of recovering the model and detecting the fault pertaining to that particular cause of the model loss. The devel...
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... electro-hydraulic system for this study is a rotational hy- draulic drive at the LITP (Laboratoire dinte´grationdinte´gration des tech- nologies de production) of the University of QuébecQuébec´QuébecÉcole de technologie supérieure ( ´ ETS). The set-up is generic and allows for simple extension of the results herewith to other electro- hydraulic systems, for example, double-acting cylinders. Re- ferring to the functional diagram in Fig. 2, a DC electric motor drives a pump, which delivers oil at a constant supply pressure from the oil tank to each component of the system. The oil is used for the operation of the hydraulic actuator and is re- turned through the servo-valve to the oil tank at atmospheric pressure. An accumulator and a relief valve are used to maintain a constant supply pressure from the output of the pump. The electro-hydraulic system includes two Moog Series 73 servo- valves which control the movement of the rotary actuator and the load torque of the system. These servo-valves are operated by voltage signals generated by an Opal-RT real-time digital control system. The actuator and load are both hydraulic motors connected by a common shaft. One servo-valve regulates the flow of hydraulic fluid to the actuator and the other regulates the flow to the load. The actuator operates in a closed-loop while the load operates open-loop, with the load torque being propor- tional to the command voltage to the load servo-valve. While the actuator and load chosen for this study are rotary drives, the exact same set-up could be used with a linear actuator and load, and thus, they are represented as generic components in Fig. 2. The test set-up includes three sensors, two Noshok Se- ries 200 pressure sensors with a 010V output corresponding to a range of 20.7MPa (3000 PSI) that measure the pressure in the two chambers of the rotational drive, as well as a tachometer to measure the angular velocity of the drive. In order to reduce the number of sensors used (a common preference for commercial application), angular displacement is obtained by numerically integrating the angular velocity measurement. Fig. 3 shows the layout of the system and the Opal-RT RT- LAB digital control system. The RT-LAB system consists of a real-time target and a host PC. The real-time target runs a dedi- cated commercial real-time operating system (QNX), reads sen- sor signals using an analog-to-digital (A/D) conversion board and generates output voltage signals for the servo-valves us- ing a digital-to-analog (D/A) conversion board. The host PC is used to generate code for the target using MATLAB/Simulink and Opal-RTs RT-LAB software and also to monitor the system. Controller parameters can also be adjusted on-the-fly from the host in RT-LAB. ...
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... electro-hydraulic system for this study is a rotational hy- draulic drive at the LITP (Laboratoire dinte´grationdinte´gration des tech- nologies de production) of the University of QuébecQuébec´QuébecÉcole de technologie supérieure ( ´ ETS). The set-up is generic and allows for simple extension of the results herewith to other electro- hydraulic systems, for example, double-acting cylinders. Re- ferring to the functional diagram in Fig. 2, a DC electric motor drives a pump, which delivers oil at a constant supply pressure from the oil tank to each component of the system. The oil is used for the operation of the hydraulic actuator and is re- turned through the servo-valve to the oil tank at atmospheric pressure. An accumulator and a relief valve are used to maintain a constant supply pressure from the output of the pump. The electro-hydraulic system includes two Moog Series 73 servo- valves which control the movement of the rotary actuator and the load torque of the system. These servo-valves are operated by voltage signals generated by an Opal-RT real-time digital control system. The actuator and load are both hydraulic motors connected by a common shaft. One servo-valve regulates the flow of hydraulic fluid to the actuator and the other regulates the flow to the load. The actuator operates in a closed-loop while the load operates open-loop, with the load torque being propor- tional to the command voltage to the load servo-valve. While the actuator and load chosen for this study are rotary drives, the exact same set-up could be used with a linear actuator and load, and thus, they are represented as generic components in Fig. 2. The test set-up includes three sensors, two Noshok Se- ries 200 pressure sensors with a 010V output corresponding to a range of 20.7MPa (3000 PSI) that measure the pressure in the two chambers of the rotational drive, as well as a tachometer to measure the angular velocity of the drive. In order to reduce the number of sensors used (a common preference for commercial application), angular displacement is obtained by numerically integrating the angular velocity measurement. Fig. 3 shows the layout of the system and the Opal-RT RT- LAB digital control system. The RT-LAB system consists of a real-time target and a host PC. The real-time target runs a dedi- cated commercial real-time operating system (QNX), reads sen- sor signals using an analog-to-digital (A/D) conversion board and generates output voltage signals for the servo-valves us- ing a digital-to-analog (D/A) conversion board. The host PC is used to generate code for the target using MATLAB/Simulink and Opal-RTs RT-LAB software and also to monitor the system. Controller parameters can also be adjusted on-the-fly from the host in RT-LAB. ...
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... Here are several key reasons why fault diagnosis is crucial for grid resilience: Early Detection of Faults: Fault diagnosis techniques enable utilities to detect grid disturbances or equipment failures early, often before they escalate into larger-scale disruptions. Early detection allows utilities to initiate timely response measures, such as rerouting power flows, isolating affected areas, or implementing corrective actions, to minimize the impact on grid operations and prevent cascading failures [89,90]. Minimization of Downtime: By identifying the root cause of grid faults or abnormalities quickly and accurately, fault diagnosis helps utilities minimize downtime and restore service to affected areas promptly [91]. ...
The increasing integration of advanced technologies within the power grid infrastructure has led to significant advancements in efficiency, reliability, and sustainability. However, this integration also introduces new vulnerabilities, particularly in the realm of cybersecurity. This paper presents an overview of smart grid cybersecurity challenges and proposes strategies for enhancing resilience through fault diagnosis techniques. Firstly, the paper examines the evolving threat landscape facing smart grids, encompassing cyber-attacks, insider threats, and natural disasters. It highlights the critical need for robust cybersecurity measures to safeguard grid operations and prevent potentially catastrophic disruptions. Next, the paper delves into various cybersecurity frameworks and standards tailored specifically for smart grids, emphasizing the importance of comprehensive risk assessment, intrusion detection systems, and secure communication protocols. Additionally, it discusses the role of machine learning and artificial intelligence in augmenting cyber defense capabilities, enabling proactive threat detection and rapid response. Furthermore, the paper explores fault diagnosis strategies aimed at maintaining grid resilience in the face of cyber incidents or physical faults. It discusses the integration of data analytics, predictive modeling, and real-time monitoring to identify and mitigate potential grid disturbances swiftly.
... The feature extraction stage is performed based on advanced signal processing approaches, which include power spectral density estimation techniques, demodulation techniques, and time-frequency analysis. The classification stage is mainly performed using machine learning approaches [89]. ...
Dynamic system monitoring is essential for ensuring the optimal performance and reliability of various systems across multiple domains. This Abstract introduces innovative approaches focusing on signal processing and parameter estimation strategies for dynamic system monitoring. Signal processing techniques such as wavelet transform and adaptive filtering are utilized for noise reduction and feature extraction from sensor data. Additionally, parameter estimation strategies including Kalman filtering and Bayesian inference aid in accurately estimating system parameters and states in real-time. These advanced methods, integrating machine learning and statistical inference, promise enhanced monitoring capabilities, facilitating proactive maintenance and fault detection in complex dynamic systems. Through case studies and simulation results, the effectiveness and versatility of these approaches in addressing real-world challenges are demonstrated, illustrating their potential for advancing the field of dynamic system monitoring.
... The role of AI and ML in mitigation efforts is expanding rapidly, providing innovative solutions and enhancing our capacity to predict, prevent, and manage various risks across multiple domains. However, ethical considerations, transparency, and accountability are essential while deploying these technologies to ensure their responsible and effective use [97]. ...
The power grid stands as a critical infrastructure supporting modern society, yet it remains susceptible to cyber threatsthat could compromise its stability and functionality. Addressing the dynamic variations and evolving challenges posedby cyber threats requires robust strategies in cybersecurity. This paper investigates methods to safeguard the stability ofthe power grid against cyber intrusions and system variations. This study delves into the multifaceted nature of cyberthreats targeting the power grid and analyzes the dynamic variations within the system that could be exploited bymalicious actors. This paper presents a comprehensive framework encompassing proactive and reactive cybersecuritymeasures. Reactive measures include incident response plans, rapid recovery protocols, and the integration of machinelearning and artificial intelligence for real-time threat detection and mitigation. Moreover, considering the interconnectednature of the power grid, this study explores collaborative approaches among stakeholders, such as utility companies,government bodies, regulatory authorities, and cybersecurity experts, to foster information sharing, best practices, andstandardized protocols. Ultimately, this paper serves as a guide for policymakers, grid operators, and cybersecurityprofessionals to develop robust strategies that safeguard the stability of the power grid in the face of evolving cyberthreats and system dynamics. By implementing a holistic cybersecurity approach, the aim is to ensure resilience,reliability, and continuity in the delivery of electricity to society
... Model-based methods generally rely on a mathematical model of the process derived from the system identification process. However, data-based methods rely only on process measurements to diagnose faults [3]. Databased detection systems can be carried out in several ways, such as the fast Fourier transform [4], wavelet transforms [5] from vibration transmission, convolutional neural networks [6], and adaptive resonance theory neural networks [7]. ...
Centrifugal pump is an instrument that is widely used in industry and has become the main driving component. A detection system is often needed to prevent damage to these pumps because they can interfere with the overall system performance. Therefore, this study discussed the development of a fault detection system for two centrifugal pump units, namely the Medium Pressure Oil Pump (MPOP) and the Water Injection Pump (WIP). In detecting the operating conditions of the pump, it was used a residual feature extraction technique in the time domain with a statistical approach. Residual was generated by using three sub-systems of a pumping system. Each sub-system was modeled using an artificial neural network with feedforward-back propagation architecture. Based on the feature values, the classifier was designed to classify pump conditions. Then the proposed fault detection system was applied in a condition monitoring system scheme. The test results (using data from the field) show that the fault detection system has an accuracy of 91.67% for MPOP and 94.8% for WIP cases. Meanwhile, the fault detection system has an accuracy above 99% during online monitoring simulations.
... The FDD approaches can be classified into different categories from the distinct aspects. According to Zhang and Jiang (2008), the existing FDD approaches can be generally classified into two categories: modelbased schemes (such as state estimation or parameter estimation technique, see in Chen et al. (2016); Liu et al. (2013)) and data-based schemes (such as neural network or expert systems techniques, see in Mahmoud and Khalid (2013); Subrahmanya and Shin (2013)). In the modelbased schemes, the output performance is decided by the disturbance and uncertainty of the model. ...
This paper presents an actuator fault and disturbance estimation strategy using Takagi-Sugeno (TS) fuzzy model. In this approach, using a coordinate transformation, the TS fuzzy system is decomposed into three different modules: state subsystem without fault and disturbance, disturbance subsystem without fault, fault subsystem without disturbance. After the transformation, the fault and disturbance can be decoupled and calculated from the input and output signals and estimation state. The convergence of TS fuzzy observer is analyzed and proved. In order to verify the performance of the proposed approach, a wind turbine system with actuator fault and disturbance have been tested, the simulation results illustrate the efficiency of the proposed strategy.
... Rupture of the anterior cruciate ligament is becoming a more prevalent issue in younger populations with increasingly more active lifestyles. Studies about ACL using magnetic resonance imaging (MRI) has shown large superiority because the structure inside knee joint can be imaged well by MRI [13]. Among the injuries of knee, ACL injury has been received higher and higher focus due to the rate of ACL injury is larger than before, especially for athletes. ...
In the reconstruction surgery of Anterior Cruciate Ligament (ACL), how to locate the anatomical position is a very hard point to clinician occupational therapists. In this paper, we propose an Anatomical Position Locating (APL) approach based on Expectation Maximization (EM) algorithm. Firstly, the proposed intersection set operation algorithm is proposed to compute the attachment region between the injured ACL and femur or tibia. Then, the anatomical position is located by the 3D points cloud with the Gaussian spatial distribution. The last, the attachment spatial distribution and the barycenter, which are also viewed as the candidates of the anatomical position by a proposed EM algorithm, is partition. Experimental results verify our assumption and demonstrate that the located anatomical position has great serviceability.
... In our inference algorithm, we compute the model parameters w t,u 's and c t 's that maximize the objective function in Eq. (3) through the Expectation-Maximization(EM) algorithm [11,26] . According to the EM algorithm, we define an arbitrary probability distribution α t u v , β t u v and δ t,s u v xy by Eqs. ...
Data integration is the process of identifying pairs of records from different databases that refer to the same entity in the real world. It has been extensively studied with regard to entity resolution, record linkage, duplicate detection or network alignment. With the increasing use of crowdsourcing platforms as a means of assessing queries manually at low cost, many studies have begun to consider ways to exploit crowdsourcing systems for efficient data integration.
In this paper, we present an efficient algorithm to integrate two graphs collected from different sources using crowdsourcing systems. Given two graphs, we repeatedly select a query node from a graph and request a human annotator to find its matching node from the other graph, which is considered to be the one indicating the same entity as the query node. The proposed method is to choose the query nodes that would increase the precision the most if it is labeled. By experiments with both the simulated answers and the labels collected by real crowdsourcing, we show that our algorithm finds more accurate graph matches with a smaller cost for crowdsourcing than the baseline algorithms.
... Further details and derivation of EM algorithm can be found in [5,28]. Using EM algorithm, mean and standard deviation for GMM model have been estimated. ...
Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation.
... The authors of [11] examined the filtering design problem for discrete-time Markov jump linear systems under the assumption that the transition probabilities are not completely known. In [12], a forward-backward-Kalman-filter-based estimation in fault diagnosis scheme is provided while a wide coverage of multi-target tracking architectures is applied to complex applications such as condition and health monitoring of aircrafts, industrial plants, and electrical infrastructures [13]. ...
A distributed estimation approach is developed in this paper using information matrix filter on a distributed tracking system in which multiple sensors are tracking the same target. The information matrix filter version is derived from covariance intersection, weighted covariance and Kalman-like particle filter, respectively. The steady performance of these filters is evaluated with different feedback strategies. The developed filters are then validated on an industrial utility boiler. Copyright
... The authors are with Department of Electrical Engineering and Computer Science, Institute Center for Energy, Masdar Institute of Science and Technology (MIST), Abu Dhabi, UAE. E-mail: mkhalid, jpeng@masdar.ac.ae earlier works from [2] and [16]. This is attained by utilizing a distributed architecture and fusing system dynamics contained within multiple substation signals. ...
... This objective is achieved by computing the oscillation state estimate α i t from each local i-th substation given a measurement sequence T + 1 for input C i T 0 and output Υ i T 0 oscillation variables. The EM algorithm [16,18] has two steps. The E −step is obtained with respect to the underlying unknown variables conditioned on the observations, thus maximizing the likelihood with respect to the oscillation states. ...
... Referring to Fig. 2, the proposed method analyzed Synchrophasor measurements collected from Bus 15,16,17,29,30,35,37,38, and 39. The predecessor, Kalman Filter, was applied to extract oscillatory information at Bus 16. ...
This paper improves the existing Kalman-based technique for detecting electromechanical oscillations using Synchrophasor measurements. The novelty is the utilization of a distributed architecture to extract maximum a-posteriori (MAP) estimations of oscillatory parameters. This was achieved by an expectation maximization (EM) algorithm. To improve initial condition estimation, initial correlation information through a forward backward (FB) Kalman-like particle filter (KLPF) was integrated into the proposed scheme. Performance evaluation was conducted using IEEE New England 39 Bus system and Synchrophasor measurements collected
from New Zealand Grid. The proposed method accurately extracted oscillatory parameters when the measurements were contaminated by continuous random small load fluctuations. The method also improved the capability of detecting multiple oscillations with similar frequencies.
