Diagnosis Of Abnormal Conditions Research Articles

A Bayesian network method using transfer learning for solving small data problems in abnormal condition diagnosis of fused magnesia smelting process

Intelligent abnormal condition diagnosis is key to the safe and stable operation of the fused magnesia smelting process (FMSP). Existing studies have applied the Bayesian network (BN) to the FMSP with sufficient data. However, learning an accurate BN model for a new FMSP using small data isn't easy. Furthermore, the unreasonable diagnosis may seriously affect product quality and security threats. To this end, we present a novel BN transfer learning (BNTL) method to resolve this abnormal condition diagnosis problem using small data. The abnormal condition diagnosis model is established offline based on the proposed BNTL method, and the occurrence or extent of abnormal conditions is identified online based on the diagnosis model and real-time process information. The BNTL involves structure and parameter transfer learning. Among them, structure transfer learning is based on score and search. A new scoring function is proposed to evaluate the local structure of the candidate target domain by weighted integration of source and target domain data based on similarity. The search process is based on the genetic algorithm with other constraints that remove loops and control the number of parent nodes. After learning the structure, parameter transfer learning is done according to a new similarity evaluation and fusion algorithm. The public network is first used to evaluate the BNTL method. Then, it is utilized to build the abnormal condition diagnosis model of the FMSP using small data. Experimental results show that the recognition accuracy is significantly improved compared with other advanced methods in a limited data environment.

Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots.

In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system.

Decomposition-Based Bayesian Network Structure Learning Algorithm for Abnormity Diagnosis Model for Coal Mill Process

In the structure learning of the large-scale Bayesian network (BN) model for the coal mill process, taking the view of the problem that the decomposition-based method cannot guarantee the sufficient learning of abnormal state node neighborhood in the diagnosis model, this paper proposes a new BN structure learning method based on decomposition. Firstly, a sketch is constructed based on an improved Markov blanket discovery algorithm and edge thickening and thinning. Second, the node centrality of k-path is used to search the important nodes, and the subgraph decomposition is realized by extracting these important nodes and their neighborhoods from the sketch. Then, through the targeted design of subgraph de-duplication, subgraph learning, and subgraph reorganization methods, the learning of large-scale BN is realized. This method is applied to public data sets, and its advantages and disadvantages are analyzed by comparing them with other methods. The advantage of the BN structure learning method of the abnormal condition diagnosis model is further verified by applying the method to the coal mill process, which is consistent with the original design intention.

A Hybrid First Principles and Data-Driven Process Monitoring Method for Zinc Smelting Roasting Process

Roasting is the first procedure in the zinc smelting process. The stable and safe operation of the roasting process is significant to guarantee the quality of output zinc and reduce industrial pollution and energy consumption. In order to realize safe and stable operation for the roasting process, it is particularly important to monitor the roasting process accurately. However, the first principles model of the roasting process is complex with coupled physical and chemical reactions, and the normal operating conditions may transfer to abnormal conditions such as over-decomposition, under-oxidation, and fluidized bed deposition, due to fluctuation of raw material composition. On the other hand, the data-driven process monitoring method will suffer from unbalanced data volume between different operating conditions, especially for the abnormal conditions, of which data are always insufficient. In order to address these problems and achieve accurate process monitoring for the zinc smelting roasting process, this article proposed a hybrid first principles and data-driven process monitoring method. In detail, an integrated principal component analysis and common subspace learning (PCA-CSL) method is first proposed to address the problem that different operating condition always has large divergence and imbalanced data volume. Here, the PCA algorithm is established for the operating conditions with sufficient data. On the contrary, for the operating condition with insufficient data, a CSL algorithm is proposed, which uses the operating condition with sufficient data to assist modeling for the operating condition with insufficient data in a common subspace, so as to realize accurate fault detection. Finally, an operating condition decision rule library is established based on integrated first principles and data-driven approach, and the parameters of decision rule were optimized according to particle swarm optimization (PSO) method to realize rule-based reasoning (RBR) based abnormal condition diagnosis. Extensive experiments are implemented to verify the effectiveness of the proposed method.

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

Condition Monitoring of Combustion Processes Through Flame Imaging and Kernel Principal Component Analysis

This article presents a methodology for the diagnosis of abnormal conditions in a combustion process through flame imaging and kernel principal component analysis (KPCA). A digital imaging system is used to capture real-time flame images and radiation signals, from which flame characteristics such as flame area, brightness, non-uniformity, and oscillation frequency are quantified. These characteristics are used as the variables to establish the KPCA model of the combustion process. With the use of Hotelling's T2 and Q statistics, the monitoring of abnormal conditions of the combustion process is achieved. Unlike the traditional principal component analysis (PCA) method, the KPCA method is capable of dealing with nonlinear data via nonlinear mapping, which projects the original nonlinear input space into a high-dimensional linear feature space. The effectiveness of the methodology is demonstrated by applying the approach to processing the data obtained on a 9MWth heavy oil fired combustion test facility. Experimental results obtained show that the KPCA method outperforms the traditional PCA in discriminating between the normal and abnormal combustion conditions, even in cases where the number of training samples is limited.

IC 센서를 이용한 고속철도차량 구동장치의 무선 온도 모니터링 시스템

Driving gear units can be affected by various problems, including those associated with external or internal defects in the bearing, problems with the lubricant oil, high-loading of the railway, and frequent impacts generated by rail joints. Temperature monitoring is a basic method in diagnosing abnormal conditions in the reduction gear and other components. This paper describes a new wireless monitoring system for the temperature diagnosis of abnormal conditions of the reduction gear. Integrated circuit (IC)-type temperature sensors were installed in the reduction gear box of a high-speed railway car. The temperature data from the reduction gear were acquired and analyzed in situ during high-speed rail operation. Analysis of these data was used to alert the driver and/or maintenance personnel when problems occurred.

Turbomachinery Abnormal Condition Diagnosis by Noise Analysis

Turbomachinery Abnormal Condition Diagnosis by Noise Analysis

Diagnosis of abnormal conditions of an aerobic SBR process

Process fault diagnosis (PFD) allows a control system to maintain the operation of a process under the presence of faults. This is a critical feature for a discontinuous activated sludge waste water treatment (WWT) process in a sequencing batch reactor (SBR), treating waste water contaminated with organic toxic compounds. Here, a methodology for diagnosis based on the extraction of characteristics from the respiration signal, a known indicator for biological activity in aerobic WWT processes, and their classification is proposed. The usefulness of the signal for the detection and classification of a set of defined abnormal conditions was verified through sensitivity analysis. The analysis not only shows the effects of parameter deviations but also indicates the characteristics to be extracted from the respiration signal for a successful classification. Results obtained by simulation indicate that the signal based PFD can successfully cope with uncertainties common in this type of bioprocesses, which prevent the straightforward application of analytical PFD approaches.

クランク軸ねじれ角度の常時監視による異常診断技術

The common theme of Shipping industry is to detect abnormal condition of the diesel engine in an early stage and to prevent it from vital accidents for increasing the Safety and the Operation rate of the ship. Although various abnomal condition diagnosis devices have been developed up to the present, in all cases, temperatures and pressures of various parts of the engine are detected and engine performance is estimated and the abnormal condition diagnosis is performed by the Artificial intelligence (A1), Therefore, the accuracy of abnomal condition diagnosis is not always satisfactory. For the analysis of egine operating performance, the combustion analysis system, etc., have also been developed Howerver, at present, these systems are not fit for actual circumstance from the point of view of sensor's durability, etc.In case of the diesel engine as Gas pressure in cylinder will act on the crankshaft via the piston and connecting rod and is converted into Torque. In this paper, the author would like to introduce way of the abnormal condition diagnosis which by the observation of Torque fluctuations (Torsion angles) of the crankshaft. We put a detecting device for the crankshaft torsion angles to the test, and validated that approach.

A diagnostic technique to detect abnormal conditions of contacts measuring vibrations in metal enclosures of gas insulated switchgear

The diagnosis of abnormal conditions at current-conducting contacts by measuring the metal-enclosure vibrations of gas-insulated switchgear was studied experimentally and theoretically. Vibrations appeared when current flowed through artificially simulated abnormal contacts. Vibration peaks existed at frequencies which were integral values times the current frequency, especially between 400 Hz and 2 kHz. Vibration peak values increased as contact voltage increased above 400 Hz. Contact voltage waveforms in the stable state were distorted and their top peaks became sharp as the contact voltage increased. The reason for the distortion was the temperature rise which accompanied the alternating current change.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Genetic counseling in obstetrics and gynecology.

SummaryIt is the increasing responsibility of the obstetrician and gynecologist to assume a role in genetic counseling. He is ideally suited for this by the very nature of his specialty, since his contact with patients covers the entire life span of womanhood. It is hoped that he will make an effort to supplement his treatment with counseling, early recognition and diagnosis of abnormal conditions and treatment of these deformities. It is suggested that by obtaining an adequate history and performing a thorough physical examination of mothers during prenatal care, and of the babies at the time of delivery, a great step may be taken in this direction.

Quantitation of human chorionic gonadotropin in urine using the slide immunologic test for pregnancy

1.1. The use of the slide immunologic test in a semiquantitative procedure for determining HCG titers in urine has been described. This method is simple, reliable, and rapid. The test can be performed in one hour.2.2. The sensitivity and accuracy of this method have been compared with those of the Friedman test and the results found have been good.3.3. The sensitivity of the immunologic test described is approximately 5 I.U. per milliliter, as determined by the use of the Second International Standard for human chorionic gonadotrophin.4.4. The geometric mean of titers observed in 295 urine samples obtained at varying times throughout pregnancy is presented. In the practical use of this test, a titer of 1:64 (320 I.U. per milliliter) is considered the high limit of normal during the first trimester of pregnancy.5.5. The potential use of this serologic test in the diagnosis of abnormal conditions like hydatidiform moles and chorionepitheliomas is discussed.