Search

Normal operating zones (NOZs) are the variation spaces of multiple related variables under normal conditions. This paper proposes a search-cone-based method to establish NOZ models and design dynamic alarm thresholds for multivariate alarm monitoring. First, mathematical models are established by exploiting search cones from historical normal data points to describe non-convex NOZs of multiple related process variables. Second, dynamic alarm thresholds of each process variable are designed based on the established NOZ models for new data points being inside or outside the NOZs. The proposed method extends from convex NOZs to non-convex ones, and the main contribution is to establish mathematical models of NOZs for monitoring abnormal conditions and design dynamic alarm thresholds for generating alarms. Continuous stirred tank reactors (CSTR) and industrial examples are provided to illustrate the effectiveness of the proposed method. Note to Practitioners—Alarm systems play critically important roles for safe and efficient operation of industrial plants. Many process variables are related so that their normal variation ranges formulate high-dimensional geometric spaces referred to as normal operating zones (NOZs). Hence, it is desirable to adopt the NOZs for alarm monitoring. By contrast, if relationships among process variables are ignored, missed and false alarms may occur to deteriorate the performance of alarm systems. This paper proposes a method to establish mathematical models for NOZs and design dynamic alarm thresholds. The established models can describe non-convex or convex NOZs; the dynamic alarm thresholds can quantify distances between NOZ boundaries and operating points of a multivariate process

Modal analysis is an important tool for structural health monitoring based on measured acceleration data, which affects the result of damage identification, safety evaluation and prediction. This paper starts with a brief review of modal identification methods, and then introduces a rigid continuous bridge-Dongying Yellow River Bridge (DYRB) and its structure health monitoring system. Amounts of acceleration data was collected from August to December 2006. The peak picking method is adopted and enhanced with selected accelerometers using average and fitting methods. As a result, the 1st transverse and first 3 vertical frequencies were identified accurately and efficiently. More researches are needed to analyze the different appearance of different mode frequencies and to verify the influence of temperature, moisture, wind, traffic load, etc.

Environment conditions such as temperature, humidity, traffic loadings and wind loadings cause changes in the identified dynamic properties of bridges, which will affect health diagnosis based on the mode frequencies in bridge structural health monitoring. This paper analyzed temperature effects on a rigid continuous bridge based on monitoring data from Aug. 2006 to Dec. 2006. First, temperature data calculating process from measured data is given and frequency time histories are graphed. Temperature effects on the bridge dynamic properties are divided into yearly temperature change and sunlight radiation. Then, theoretical eigenfrequency functions of a simply-supported uniform beam with and without axial force are provided to explain and distinguish temperature effects and constraints on characteristic frequencies. Relationship of measured temperature and frequencies data are drafted and linear correlations are found on the 1st and 3rd vertical frequencies. 2nd vertical frequency fluctuated cyclically in one day, caused by sunlight radiation and restrains.