BS ISO 13374-2-2007 Condition monitoring and diagnostics of machines Data processing communication and presentation Data processing《机器的工况监测和诊断 数据处理 通信和表示 数据处理》.pdf
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2、onitoring and diagnostics of machines Data BRITISH STANDARDBS ISO 13374-2:2007BS ISO 13374-2:2007This British Standard was published under the authority of the Standards Policy and Strategy Committee on 31 October 2007 BSI 2007ISBN 978 0 580 55407 0Amendments issued since publicationAmd. No. Date Co
3、mmentscontract. Users are responsible for its correct application.Compliance with a British Standard cannot confer immunity from legal obligations.National forewordThis British Standard is the UK implementation of ISO 13374-2:2007.The UK participation in its preparation was entrusted by Technical Co
4、mmittee GME/21, Mechanical vibration, shock and condition monitoring, to Subcommittee GME/21/7, Condition monitoring.A list of organizations represented on this committee can be obtained on request to its secretary.This publication does not purport to include all the necessary provisions of a Refere
5、nce numberISO 13374-2:2007(E)INTERNATIONAL STANDARD ISO13374-2First edition2007-07-15Condition monitoring and diagnostics of machines Data processing, communication and presentation Part 2: Data processing Surveillance et diagnostic dtat des machines Traitement, change et prsentation des donnes Part
6、ie 2: Traitement des donnes BS ISO 13374-2:2007ii iiiContents Page Foreword iv Introduction v 1 Scope . 1 2 Normative references . 1 3 CM time-order/time-reference data, normally referenced with UTC and local time zone; data quality indicator (e.g. “bad”, “good”, “unknown”, “under review”, etc.). Ex
7、amples of digitized data include: floating point values for scalar data; magnitude and time series for dynamic data; thermal radiation data with digitized image for thermographic data; sample test results for lubricating fluid/air/water sample data. BS ISO 13374-2:200774.3 Data Manipulation (DM) blo
8、cks As detailed in Figure 4, the DM block processes the digital data from the DA block to convert it to a desired form which characterizes specific descriptors (features) of interest in the machine condition monitoring and diagnostic process. Often the functionality within this layer consists of som
9、e signal processing algorithms. Figure 4 Data Manipulation block This block may contain speciality processing functions such as Fast Fourier Transforms, wavelets or simple average values over a time interval. Examples of the descriptor outputs of the DM block include: extracted feature; conversion f
10、rom time domain to frequency domain and vice versa; calculated, non-interpretative values; virtual sensor (differential pressure from inlet and outlet pressures); integrating acceleration to velocity/double integration to displacement; filtering; normalization; time series including sample rate. BS
11、ISO 13374-2:20078 4.4 State Detection (SD) blocks As shown in Figure 5, the primary function of the SD block (sometimes referred to as “state awareness”) is to compare DM and/or DA outputs against expected baseline profile values or operational limits, in order to generate enumerated state indicator
12、s with respective boundary exceedances. The SD block generates indicators which may be utilized by the Health Assessment block to generate alerts and alarms. When appropriate data are available, the SD block should generate assessments based on operational context, sensitive to the current operation
13、al state or operational environment. Figure 5 State Detection block Typically, this block of processing provides data which will contribute to a diagnosis in the health assessment block. The SD block may make use of current and historical DA and DM outputs to evaluate the current state. It may provi
14、de data manipulation and sensor module control signals, such as acquisition scheduling commands, data triggers and processing instructions. Examples of outputs of the SD block include: enumerated state indicator; threshold boundary alerts; severity of threshold boundary deviation above/below; rate o
15、f change alert; BS ISO 13374-2:20079 degree of abnormality; statistical analysis using parametric and non-parametric approaches, e.g. Weibull and Gaussian distribution. 4.5 Health Assessment (HA) blocks As shown in Figure 6, the HA block is an information block which utilizes expertise from a human
16、or automated agent to determine the current health of the equipment and to diagnose existing fault conditions. It determines the state of health and potential failures by fusing the outputs of the DA, DM, SD and other HA blocks. Figure 6 Health Assessment block An output of this block includes the c
17、omponent/systems current health grade and diagnosed faults and failures with associated likelihood probability. A calculation of the current risk priority number (RPN) may also be performed. Modelling of ambiguity groups and multiple hypotheses may be included in the output data structures. The HA b
18、lock may also output an explanation detailing the evidence for a diagnosis or health grade. BS ISO 13374-2:200710 4.6 Prognostic Assessment (PA) blocks As shown in Figure 7, the primary function of the PA block is to project the future state of the monitored equipment using a combination of prognost
19、ic models and their algorithms, including future operational usage model(s). This block determines the future state of health and failure modes by combining the relevant outputs of the DA, DM, SD, HA and other PA blocks and applying a prognostic algorithm or model based on supplied projected operati
20、onal utilization. To aid the algorithm or model, the HA block may also retrieve account historical failure data and operational history, along with projected failure rates related to operational utilization. The prognostics layer may report health grade at a future time or may estimate the remaining
21、 life of an asset given its projected usage profile. Assessments of future health or remaining life may also have an associated prognosis of the projected fault condition. A calculation of the future risk priority number (RPN) may also be performed. An output of this block includes the component/sys
22、tems future health grade and future failure events with associated likelihood probability. Modelling of ambiguity groups and multiple hypotheses may be included in the output data structures. The PA block may also output an explanation detailing the evidence for a proposed failure event or health gr
23、ade. Figure 7 Prognostic Assessment block BS ISO 13374-2:2007114.7 Advisory Generation (AG) blocks As detailed in Figure 8, the primary function of the AG block is to integrate information from DA, DM, SD, HA, PA and other AG blocks and external constraints (safety, environmental, budgetary, etc.),
24、and to provide optimized recommended actions and alternatives to applicable personnel or external systems. Recommendations may include prioritized operational and maintenance actions and capability forecast assessments or modifying operational profiles to allow mission completion. The decision suppo
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