STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

A Preliminary Study of C-reactive Protein in the Diagnosis and Monitoring of Lyme Disease

Concentrations of C-reactive protein (CRP) in the serum of 14 patients suffering from Lyme diseasc were measured. 86% of these patients were found to have abnormally high concentrations of serum CRP (range 14-158 mg/L). The CRP concentration of a 60-yearold patient abated from 29 mg/L to 13 mg/L after treatrnent. Results suggest that serum CRP concentration can provide a valuable and accurate means for the clinical diagnosis and monitoring of Lyme disease

Data Quality Assurance Techniques for a Monitoring and Diagnosis System

By researching the data quality problem in the monitoring and diagnosis system （MDS） , the method of detecting non-condition data based on the development trend of equipment condition is proposed, and three requirements of criteria for detecting non-condition data： dynamic, syntheses and simplicity are discussed. According to the general mode of data management in MDS, a data quality assurance system （DQAS） comprising data quality monitoring, data quality diagnosis, detection criteria adjusting and artificial confirmation is set up. A route inspection system called MTREE realizes the DQAS. Aiming at vibration data of route inspection, two detecting criteria are made. One is the quality monitoring parameter, which is found through combining and optimizing some fundamental parameters by genetic programming （GP）. The other is the quality diagnosis criterion, i. e. pseudo distance of Spectral-Energy-Vector （SEV） named Adjacent J-divergence, which indicates the variation degree of adjacent data＇s spectral energy distribution. Results show that DQAS, including these two criteria, is effective to improve the data quality of MDS.

A Study on a Remote Monitoring and Diagnosis System and Its Application

Remote monitoring and diagnosis （RMD） is a new kind of monitoring and diagnosis technology that combines computer science, communication technology and fault diagnosis technology. Via the Internet a remote monitoring and diagnosis system can be established. In this paper, the model of an Internet based remote monitoring and diagnosis system is presented; the function of every part of the RMD system is discussed. Then, we introduce a practical example of a remote monitoring and diagnosis system that we established in a factory; its traits and functions are described.

Research on remote hydro-generator sets diagnosis system

A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.

In-service Structural Health Monitoring of a Full-scale Composite Horizontal Tail

In-service structural health monitoring（SHM） technologies are critical for the utilization of composite aircraft structures. We developed a Lamb wave-based in-service SHM technology using built-in piezoelectric actuator/sensor networks to monitor delamination extension in a full-scale composite horizontal tail. The in-service SHM technology combine of damage rapid monitoring（DRM） stage and damage imaging diagnosis（DID） stage allows for real-time monitoring and long term tracking of the structural integrity of composite aircraft structures. DRM stage using spearman rank correlation coeffi cient was introduced to generate a damage index which can be used to monitor the trend of damage extension. The DID stage based on canonical correlation analysis aimed at intuitively highlighting structural damage regions in two-dimensional images. The DRM and DID stages were trialed by an in-service SHM experiment of CFRP T-joint. Finally, the detection capability of the in-service SHM technology was verified in the SHM experiment of a full-scale composite horizontal tail. Experimental results show that the rapid monitoring method effectively monitors the damage occurrence and extension tendency in real time; damage imaging diagnosis results are consistent with those from the failure model of the composite horizontal tail structure.

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

CLINICAL VALUE OF URINARY AND SERUM PSEUDOURIDINE IN DIAGNOSIS AND MONITORING OF PRIMARY LIVER CANCER

Urinary and serum pseudouridine concentrations were determined by high-performance liquid chromatography in 80 patients with primary liver cancer, 32 with benign space occupying lesions of the liver, 42 with liver cirrhosis and 40 healthy subjects. Their mean urinary and serum pseudouridine levels were 39.2=11.5 nmol / /μmol creatinine and 3.4 ± 1.3 μmol / L, 24.5 = 5.4 nmol / μmol creatinine and 2.5 = 0.5 μmol / L, 22.8 ± 7.8 nmol / μmol creatinine and 2.3 = 0.4 μmol / L, 26.4 ± 4.6 nmol / μmol creatinine and 2.3 = 0.4 μmol / L, respectively. Exceeding the mean plus 2SD of pseudouridine of healthy control was considered as positive value for the diagnosis of primary liver cancer. Thus the positivity of urinary and serum pseudouridine in hepatoma was 71.3% and 70.0%, respectively. The positive rate of combined pseudouridine and alpha-fetoprotein assay was 91.3% in patients with hepatoma. Besides, pseudouridine levels could elevate before positive localization and reduce to normal levels after tumor resection. The results showed that the determination of pseudouridine is of clinical significance in the diagnosis and monitoring of primary liver cancer.

Intelligent Information Management in Aquaponics to Increase Mutual Benefits

Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.

睡眠呼吸监测的简便化

阻塞性睡眠呼吸暂停低通气综合征（OSAHS）在人群中发病率较高,我国上海地区流行病学调查显示,年龄〉30岁人群OSAHS患病率约为3.82%[1]。研究证实OSAHS与心脑血管疾病关系密切。