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The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action...
This paper proposes a novel approach for machine fault diagnosis using industrial wireless sensor networks (IWSNs) and on-sensor calculation. In this paper, the induction motor and vibration signal are taken as an example of the monitored industrial equipment and signal due to their wide use. The discrete wavelet transform and wavelet energy-moment are used for on-sensor machine fault feature extraction,...
Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD)...
In sensor-based activity recognition often huge amounts of data have to be acquired from multiple sensors, which need to be communicated for further processing. When using wireless sensor nodes, energy efficiency is of outstanding importance, since it directly influences the time until the battery needs to be recharged. However, communicating a huge amount of sensor data over wireless interfaces causes...
Link prediction is a fundamental problem in social systems, including the prediction of user interaction and the links between users and items, which is also referred to as recommendation. Previous works mainly focus on the prediction task independently, which predict either the links between users or the links between users and items. However, these two prediction tasks are always coupled with each...
Rolling element bearings have a pivotal role in rotating machine and their failures are the leading cause of more substantial failures in the machine. In response to their importance, there is a growing body of research looking at condition monitoring of rolling element bearings to avoid machine breakdowns. In this study, by taking advantages of Compressive Sampling (CS), Laplacian Score (LS) and...
The high penetration of Wind Turbine (WT) in the grid is a promising solution to increase the electricity production with renewable energies. In this work, we propose a data-driven methodology for dip voltage fault detection and diagnosis. From experimental measurements the current vector trajectory deformation in the (αβ) reference frame is derived and a statistical-based analysis (first four statistical...
In RFID systems, it is unavoidable that we read stray tags occasionally and it is critical that we can reliably filter out stray tag readings for accurate tracking. In this paper, we studied a set of features namely RSSI Euclidean distance (RED), number of reads Euclidean distance (NED) and time between reads Euclidean distance (TED) for classifying RFID reading into target and stray class membership...
In this paper, we propose line current sensor fault detection for AC drives. The method is based on the measured currents and the features are extracted either in the natural reference frame or in the transformed Park synchronous rotating frame. The features are the first four statistical moments or the Kullback Leibler Divergence (KLD) of the Probability Density Functions (PDF). For offset fault,...
This paper presents a novel method for fault detection and location in modular five-level converters (MFLC) based on stacked sparse autoencoder (SSAE). SSAE is composed of multiple SAE and a softmax classifier. The capacitor voltage signals of all sub-modules (SMs) in the MFLC circuit are combined into a multi-channel signal. By moving window along the multi-channel signal, a set of signal segments...
Condition monitoring of machinery has entered the big data era, while the existence of dirty data reduces the quality of the whole data. In order to recognize the dirty data included in machinery monitoring data, a new method is proposed in this paper. First, a feature named sampled power index (SPI) is designed to transform the dirty data recognition issue into the outlier recognition. Then the windowing...
Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy...
For a wide range of applications in industry, it is sometimes necessary to perform acoustic source localization. In this paper, a passive sound localization and classification system is designed and implemented. Each sensor consists of a microphone array which is used to detect the direction-of-arrival (DoA) of an acoustic signal. Multiple DoA sensors can be combined to form a wireless sensor network...
We propose a framework to extend corner feature detection in standard rectangular images with less distortion to distorted circular images captured with fisheye lenses. To solve two problems of nonuniformity of spatial resolution and spherical polar coordinates singularity, our approach makes use of a modification in the Yin-Yang grid, which is an overset grid consisting of two latitude/longitude...
In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Cumulated Sum (CUSUM) efficiency is explored for incipient fault detection. The fault is assumed to be a Gain variation, an Offset evolution, a Phase shifting or one of the multiple...
Different with the onshore power generation equipment, marine current turbine always operates in a poor natural environment that the arrangement of measured points is restricted. With a small amount of signal, single domain usually cannot detect the fault comprehensively. A multi-domain reference method is proposed in this paper for imbalance fault detection of variable-speed direct-drive marine current...
In the practical engineering of cement vertical mill, many faults occur in the bearings, including the driving end and the fan end. This paper introduces the structure of common bearing, and analyzes the potential fault position and the characteristic frequency when fault occurs. Furthermore, an improved empirical mode decomposition algorithm is adopted to study the fault characteristic of bearings...
A real-time hardware architecture based on scale-invariant feature transform algorithm (SIFT) feature extraction with parallel technology has been introduced in this paper. The proposed parallel hardware architecture could be able to extract feature via a Field-Programmable Gate Array (FPGA) chip efficiently, which provided the real-time performance and the similar accuracy with software implementation...
Monitoring workplace activities (what, who, when and where) is beneficial for workforce management. Wearable Recognition enables efficient activities recognition and inferring workplace activities from wearable sensors is attractive but complex. This paper studied the feature extraction of wearable sensory data to uncover the applicability and robustness of wearable sensing approach to activity recognition...
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