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GB/T 41344.2-2022 PDF in English

GB/T 41344.2-2022 (GB/T41344.2-2022, GBT 41344.2-2022, GBT41344.2-2022)
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GB/T 41344.2-2022: PDF in English (GBT 41344.2-2022)

GB/T 41344.2-2022
ICS 13.110
CCS J 09
Safety of machinery -- Risk early-warning -- Part 2: Monitor
Issued by: State Administration for Market Regulation;
Standardization Administration of PRC.
Table of Contents
Foreword ... 3
Introduction ... 5
1 Scope ... 7
2 Normative references ... 7
3 Terms and Definitions ... 7
4 Early-warning monitoring process ... 8
5 Determination of monitoring variables ... 9
6 Monitoring data processing ... 10
6.1 Composition of a data processing module ... 10
6.2 Data collection (DC) ... 11
6.2.1 Composition ... 11
6.2.2 Basic requirements ... 12
6.2.3 Data collection method ... 12
6.3 Data manipulation (DM) ... 14
6.3.1 Composition ... 14
6.3.2 Basic requirements ... 15
6.3.3 Data manipulation process ... 15
6.4 Data analysis (DA) ... 16
6.4.1 Composition ... 16
6.4.2 Basic requirements ... 16
6.4.3 Data analysis methods ... 17
6.5 Data output (DO) ... 17
6.5.1 Composition ... 17
6.5.2 Basic requirements ... 18
6.5.3 Data output format ... 18
6.6 Data archiving and information presentation ... 18
Appendix A (Informative) Examples of monitoring variables ... 19
References ... 21
Safety of machinery -- Risk early-warning -- Part 2: Monitor
1 Scope
This document specifies the early-warning monitoring process, determination of
monitoring elements, and monitoring data processing for the risk early-warning of
This document is applicable to the monitoring of multi-aspect data related to the safety
of machinery, such as own factors of machinery/machines, environmental factors, and
operator factors.
2 Normative references
The following documents are essential to the application of this document. For the dated
documents, only the versions with the dates indicated are applicable to this document;
for the undated documents, only the latest version (including all the amendments) is
applicable to this standard.
GB/T 30174-2013 Safety of machinery -- Terms
GB/T 41344.1-2022 Safety of machinery -- Risk early-warning -- Part 1: General
3 Terms and Definitions
The terms and definitions defined in GB/T 30174-2013, GB/T 41344.1-2022 and the
followings apply to this document.
3.1 Monitor
The process of obtaining the real-time data of the target object through the perception
and transmission technologies, processing and analyzing the data according to the
agreed method, and outputting the result.
3.2 Monitored object
The target object or parameter to be monitored and measured according to the
requirements of risk early-warning.
Note: The target objects are usually people, machines, and some factors in the working environment such
as sound, light, and heat.
3.3 Monitored area
The target space or range that is determined according to the requirements of risk early-
3.4 Monitoring variable
The attributes or performance characteristics of the target object to be monitored and
measured according to the requirements of risk early-warning.
Note: The attributes or performance characteristics of the target object are usually the spatial position of
human, the operating state of the machine, the intensity of the noise, the vibration frequency, the dust
concentration, etc.
3.5 Monitoring data
The raw data of the monitoring content obtained during the monitoring process and the
processed result data.
3.6 Behavior track of human
Conscious or unconscious movements, expressions, and other subtle actions of people
when operating machines.
3.7 Spatial position of human
The three-dimensional coordinates of the person in his workspace when the danger
source is being taken as the origin of the reference system.
4 Early-warning monitoring process
The monitoring process of risk early-warning for machinery/machines generally
includes: the determination of monitoring variables, data collection, data manipulation,
data analysis, and data output; the schematic diagram is shown in Figure 1.
The main contents of the monitoring process of risk early-warning include:
a) Determination of monitoring variable: determine variables such as people,
machines, environment, and their compound effects;
b) Data collection: obtain the real-time data from target objects through perception
and transmission technology;
c) Data manipulation: carry out the filtering, amplifying, and other related processes
on the collected information to make it available for data analysis;
Each module shall have the following functions.
a) Data Collection (DC): Use the related sensors to collect the status information of
people, status information of machines, and the status information of the
b) Data Manipulation (DM): The process of converting the information into valid
information that can be used for data analysis by classifying, filtering, and
amplifying the collected information.
c) Data Analysis (DA): Carry out the trend inference analysis on the processed valid
data according to a certain model.
d) Data Output (DO): The process of outputting the analyzed data set to the specified
location of the system in the form of historical data and real-time data.
e) Data archiving and information presentation: storage and display of monitoring
Note: This document does not involve the influence of errors and the transfer of errors within the data
processing modules. Error sources include instrument calibration, signal conditioning and processing,
environmental noise, rounding of calculation results, anthropogenic input, and the combined influence
of the above factors.
6.2 Data collection (DC)
6.2.1 Composition
The data collection module provides the system with an entrance to obtain digital
information, and the digital information can enter the module by automatic or manual
methods. The data collection can input the analog signal of the sensor, and can also
collect and combine the sensor signal from the data bus. The schematic diagram of the
data collection module is shown in Figure 3.
The output of the data collection includes the following:
--- Digital data;
--- Time series data;
--- Data quality indicator (for example, “Excellent”, “Poor”, “Unknown”,
--- Mixed collection: The fixed and mobile monitoring are carried out
complementary. Collection interval
Whether continuous or periodic sampling, the measurement time interval needs to be
considered. The measurement interval mainly depends on the monitoring variables and
the change rate of the related parameters. At the same time, the influence of factors such
as the operation cycle, cost, and criticality of the monitoring device also need to be
taken into account when considering the measurement time interval. Collection rate
For the monitoring of people, the presence or absence of security protection and the
working status shall be monitored in real-time.
For the monitoring of the machine, the data collection rate shall be fast enough to
capture a complete data set before its operating conditions change.
For the monitoring of the environment, the data shall be collected in real-time, and the
monitoring data that is not abrupt or difficult to change can be collected by time-sharing. Location of collection
For the monitoring of people, the placement of the monitoring device shall be such that
the monitoring area can cover the entire working area of the personnel, and the device
can accurately capture the persons’ behavior trajectories and positions.
For the monitoring of the machine, the placement of the monitoring device shall be
selected at where the changes of the monitoring variables can be most measured, and
the status information data of the machine can be accurately collected.
For the monitoring of the environment, the monitoring devices shall be placed in as
many locations as possible, so that they can monitor multiple parameters such as
ambient temperature, humidity, noise, dust, combustible gas, and sparks, through
various sensors at different positions.
Each monitoring point shall have a unique identification. Permanent tags or
identification numbers should be used. For each location of collection, the factors that
need to be considered also include but are not limited to:
--- Safety;
--- Sensitivity to the change of fault status;
--- Reduction of sensitivity to other influencing factors;
--- Repeatability of measurement;
--- Signal attenuation or loss;
--- Accessibility;
--- Environmental impact;
--- Cost.
6.3 Data manipulation (DM)
6.3.1 Composition
The data manipulation module processes the data from the data collection and converts
them into the required forms. The function of this module is to convert the collected
information into effective information for data analysis by classifying, filtering, and
amplifying the collected information. This module includes specialized processing
functions (such as fast Fourier transform, and wavelet transform) or simple averaging
carried out with intervals of time. The schematic diagram of the data manipulation
module is shown in Figure 4.
Examples of the outputs of data manipulation are as follows:
--- Feature extraction;
--- Mutual conversion between time domain and frequency domain;
--- Calculated intermediate value;
--- Virtual sensor (pressure difference between input and output ports);
--- Filtering;
--- Normalization;
--- Time series containing sampling rate.
Figure 6 -- Data output module
6.5.2 Basic requirements
The data output shall meet the following requirements:
--- Output data in a specified format;
--- Ensure the validity of real-time data;
--- Output data can be connected with the early-warning classification.
6.5.3 Data output format
The data output forms mainly include the real-time data and the historical data. The
real-time data is mainly used for real-time early-warning classification, and the
historical data is mainly used for data analysis. See GB/T 41344.3 for the content of
risk early-warning classification for the safety of machinery.
6.6 Data archiving and information presentation
In the process of monitoring data processing, data archiving is very important.
Historical data can be analyzed for statistical correlation. The data archiving system
formulates the archiving rate and the amount of data that needs to be archived.
Information from data collection (DC), data manipulation (DM), and data analysis (DA)
is displayed through the information presentation module. It is important to convert data
into a form that can express the information clearly because the information is necessary
for correct decision-making.
Source: Above contents are excerpted from the PDF -- translated/reviewed by: www.chinesestandard.net / Wayne Zheng et al.