GB/T 41344.2-2022Safety of machinery—Risk early-warning—Part 2: Monitor (English PDF)

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- warning.

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.

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", "Evaluating").

--- Mixed collection: The fixed and mobile monitoring are carried out complementary.

6.2.3.2 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.

6.2.3.3 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.

6.2.3.4 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.