Patent Description:
<CIT>, relates to an intelligent condition monitoring and fault diagnostic system for preventative maintenance.

<CIT>, relates to a system and method for providing a monitoring of industrial equipment.

<CIT>, relates to an anomaly detection method.

Equipment maintenance is the most important in the manufacturing industry. Improper equipment maintenance and any equipment fault will lead to a low efficiency and a high safety risk and especially cannot be borne in a process industry.

Once formed, a product is difficult to recover in a process industry. That is to say, the product cannot be disassembled once assembled. Physical or chemical methods such as mixing, separation, pulverization and heating are mainly used for raw materials in a process industry to increase the value of raw materials. Typical process industries include the pharmaceutical industry, the chemical industry, the petrochemical industry, the power industry, the steelmaking industry, the energy industry, the cement industry, the food industry, the beverage industry, etc. Enterprises in the process industries mainly adopt the make-to-stock, batch and continuous production methods.

The equipment of an enterprise in the process industries is a fixed production line with a large investment and a fixed process flow. The equipment has a limited production capacity. The maintenance of equipment in the production line is especially important and no fault is allowed. An equipment fault in the process industries will lead to a forced stop of the production process which involves many chemical reactions, and most intermediate products produced from incomplete reactions caused by the production stop will be discarded only as rejects. The incurred losses of raw materials and energy are enormous. In addition, in most cases, the equipment needs to be restarted after a production stop, and it will take the equipment some time to get stabilized and the efficiency and the safety will be influenced. Therefore, it is necessary to online monitor the state of the equipment so as to reduce unplanned stops by detecting the "health" problem of the equipment as early as possible.

However, it is very difficult to determine the health condition of the online equipment or obtain the fixed rules for monitoring the lifecycle state of the equipment, especially in a process industry where complex and highly sensitive correlations between a large number of variables are involved, because of a lack of an accurate definition of the boundary between the normal and abnormal equipment states.

Most manufacturers still perform planned routine maintenances, and in this case, the equipment runs until a scheduled maintenance time. This policy is non-real-time and highly dependent on experience. As a result, an excess of maintenances is caused because the maintenance time tends to be selected before any potential fault or a lack of maintenances is caused because it is almost impossible to provide all different fault modes in advance.

In view of this, one object of the present invention is to provide a method for monitoring the state of a device in a process industry to realize the online monitoring of the state of the device in the process industry;.

To achieve the above-mentioned objects, the following technical solutions are provided in the present invention:
A method according to claim <NUM> is provided.

After obtaining the correlated state dimensions, the method further comprises:
outputting the obtained correlated state dimensions to the user, and if receiving modification information input by the user about the correlated state dimensions, modifying the correlated state dimensions according to the modification information.

After obtaining the device state reference model, the method further comprises:
outputting the obtained device state reference model to the user, and if receiving modification information input by the user about the device state reference model, modifying the device state reference model according to the modification information.

The correlation analysis algorithm is the Pearson correlation algorithm and/or the Kendall correlation algorithm, and
the algorithm for establishing a device state reference model is the Dirichlet process algorithm based on the Gaussian mixture model.

Also provided is an apparatus according to claim <NUM>.

The data acquisition module collecting and saving data of the multi-dimensional states of a device in a process industry is specifically used to.

The apparatus further comprises a man-machine interaction module, and
the correlation calculation module is further used to output the obtained correlated state dimensions to the user through the man-machine interaction module, and if receiving modification information input by the user about the correlated state dimensions from the man-machine interaction module, modify the correlated state dimensions according to the modification information.

The apparatus further comprises a man-machine interaction module, and
the state reference establishment module is further used to output the obtained device state reference model to the user through the man-machine interaction module, and if receiving modification information input by the user about the device state reference model from the man-machine interaction module, modify the device state reference model according to the modification information.

A computer readable storage medium according to claim <NUM> and a computer program according to claim <NUM> are also provided.

An apparatus for monitoring the state of a device in a process industry comprises a processor and a memory, and
applications, which can be executed by the processor to enable the processor to perform the steps of the above-mentioned method for monitoring the state of a device in a process industry, are stored in the memory.

From the above-mentioned technical solutions, it can be seen that in the present invention, the correlated state dimensions are obtained by performing a correlation analysis of history data of the multi-dimensional states of a device, then a device state reference model is established by using the history data of the correlated dimensional states, and finally the device state reference model is used to determine the real-time data of the correlated dimensional states to determine whether the state of the device is normal, thus realizing the online monitoring of the state of the device.

The following detailed description in combination with the drawings will make more obvious other characteristics, features and advantages of the present invention.

To make clearer the objects, technical solutions and advantages of the present invention, the following further describes in detail the present invention in combination with the drawings and embodiments.

"One" or "the" in the singular form in the description or claims of the present invention is also intended to cover the plural form, unless otherwise explicitly specified in the document.

The method for monitoring the state of a device in a process industry provided by the present invention comprises: collecting and saving data of the multi-dimensional states of a device in a process industry; adopting a preset correlation analysis algorithm to perform a correlation analysis of the collected history data of the multi-dimensional states of the device to obtain the correlated state dimensions; acquiring history data of correlated dimensional states according to the obtained correlated state dimensions and adopting a preset algorithm for establishing a device state reference model to model the acquired history data of correlated dimensional states to obtain the device state reference model; acquiring real-time data of the corresponding correlated dimensional states of the device state reference model in real time and determining whether the current state of the device is normal according to a preset device state determination condition, the real-time data of the correlated dimensional states and the device state reference model.

The step of collecting and saving data of the multi-dimensional states of a device in a process industry can comprise collecting data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system, and the simulation system and saving the data, or collecting data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and saving the data.

In order to obtain the more accurate result of the correlated state dimensions, after obtaining the correlated state dimensions, the method further comprises outputting the obtained correlated state dimensions to the user, and if receiving modification information input by the user about the correlated state dimensions, modifying the correlated state dimensions according to the modification information.

In order to obtain a more accurate device state reference model, after obtaining the device state reference model, the method further comprises outputting the obtained device state reference model to the user, and if receiving modification information input by the user about the device state reference model, modifying the device state reference model according to the modification information.

Wherein, the correlation analysis algorithm can be the Pearson correlation algorithm and/or the Kendall correlation algorithm, and the algorithm for establishing a device state reference model can be the Dirichlet process algorithm based on the Gaussian mixture model.

The apparatus for monitoring the state of a device in a process industry provided by the present invention comprises: a data acquisition module, collecting and saving data of the multi-dimensional states of a device in a process industry, a correlation calculation module, adopting a preset correlation analysis algorithm to perform a correlation analysis of the history data collected by the data acquisition module of the multi-dimensional states of the device to obtain the correlated state dimensions, a state reference model establishment module, acquiring history data of correlated dimensional states from the data acquisition module according to the correlated state dimensions obtained by the correlation calculation module and adopting a preset algorithm for establishing a device state reference model to model the acquired history data of correlated dimensional states to obtain the device state reference model, and an online monitoring module, acquiring real-time data of the corresponding correlated dimensional states of the device state reference model in real time from the data acquisition module according to the device state reference model established by the state reference model establishment module and determining whether the current state of the device is normal according to a preset device state determination condition, the real-time data of the correlated dimensional states and the device state reference model.

Wherein, the data acquisition module collecting and saving data of the multi-dimensional states of a device in a process industry is specifically used to collect data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system and save the data, or collect data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and save the data.

The apparatus further comprises a man-machine interaction module, and the correlation calculation module is further used to output the obtained correlated state dimensions to the user through the man-machine interaction module, and if receiving modification information input by the user about the correlated state dimensions from the man-machine interaction module, modify the correlated state dimensions according to the modification information.

The apparatus further comprises a man-machine interaction module and the state reference establishment module is further used to output the obtained device state reference model to the user through the man-machine interaction module, and if receiving modification information input by the user about the device state reference model from the man-machine interaction module, modify the device state reference model according to the modification information.

The computer readable storage medium provided by the present invention stores a computer program and the computer program performs the steps of the above-mentioned method for monitoring the state of a device in a process industry when executed by a processor.

Another device for monitoring the state of a device in a process industry provided by the present invention comprises a processor and a memory, and applications, which can be executed by the processor to enable the processor to perform the steps of the above-mentioned method for monitoring the state of a device in a process industry, are stored in the memory.

<FIG> is a flowchart of the method for monitoring the state of a device in a process industry in one embodiment of the present invention, and the specific steps are as follows:
Step <NUM>: Collect and save data of the multi-dimensional states of a device in a process industry.

Specifically, data of the multi-dimensional states of the device in the process industry can be collected from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system and can be saved, or data of the multi-dimensional states of the device in the process industry can be collected from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and can be saved.

Here, the data of each dimensional state corresponds to a time sequence data flow for describing a state of a device. For example, if n sensors (for example, a temperature sensor, a vibration sensor, a pressure sensor, etc.) are deployed on a device to detect a plurality of states of a plurality of components of the device, data of n dimensional states of the device will be collected.

Step <NUM>: Adopt a preset correlation analysis algorithm to perform a correlation analysis of the collected history data of the multi-dimensional states of the device to obtain the correlated state dimensions.

The correlation analysis algorithm is, for example, the Pearson correlation algorithm or the Kendall correlation algorithm.

For example, data of n dimensional states of a device is collected, it is learned that data of m (m≤n) dimensional states is correlated after a correlation analysis, and the dimensional values of the m dimensional states are <NUM>, <NUM>, <NUM>, <NUM>. , respectively.

According to the invention, one of the dimensional states is used as a target state, and then a correlation analysis algorithm is used to analyze which dimensional states are correlated to the target state.

Step <NUM>: Acquire history data of correlated dimensional states according to the obtained correlated state dimensions and adopt a preset algorithm for establishing a device state reference model to model the acquired history data of correlated dimensional states to obtain the device state reference model.

The algorithm for establishing a device state reference model is, for example, the Dirichlet process algorithm based on the Gaussian mixture model.

For example, if it is learned from the analysis by use of a correlation analysis algorithm that a plurality of state dimensions correlated to a target state dimension (for example, state dimension p) are dimensions q, u and v, respectively, then an attempt is made to establish a mapping when the device state reference model is established. In the mapping, history data of state dimensions q, u and v is used as input parameters and history data of state dimension p is used as output parameter. The mapping is the device state reference model, that is to say, the device state reference model depicts the relationship between state dimension p and state dimensions q, u and v.

A device state reference model can be established for each target state dimension of different target state dimensions of a device.

Step <NUM>: Acquire real-time data of the corresponding correlated dimensional states of the device state reference model in real time according to the device state reference model and determine whether the current state of the device is normal according to a preset device state determination condition, the real-time data of the correlated dimensional states and the device state reference model.

For example, real-time data of state dimensions p, q, u and v is acquired in real time according to the device state reference model established to depict the relationship between state dimension p and state dimensions q, u and v, then real-time data of state dimensions q, u and v is input into the device state reference model for calculations to obtain an expected data value of state dimension p, the difference between the expected data value of state dimension p and the real-time acquired actual data value of state dimension p is calculated, it is determined whether the difference is less than a preset threshold, if yes, the state of dimension p is considered normal, and otherwise, the state of dimension p is considered abnormal.

The device state determination condition can be: if the difference between the expected data value of the device target state obtained through calculations according to the device state reference model and the actual data value of the device target state is less than a preset threshold, the device target state is considered normal, and otherwise, the device target state is considered abnormal. The specific device state determination condition can be set by the user according to the application scenario.

Specifically, after the current state of a device is determined to be normal or abnormal, the determined current state of the device can be output to the user, and the user can modify the current state of the device according to experience. If the user modifies the current state of the device according to experience, the corresponding device state reference model can be considered inaccurate, and the real-time data acquired in Step <NUM> of corresponding correlated dimensional states of the device state reference model is used as history data to re-establish a device state reference model.

<FIG> shows the structure of the apparatus <NUM> for monitoring the state of a device in a process industry in one embodiment of the present invention, and the apparatus <NUM> comprises a data acquisition module <NUM>, a correlation calculation module <NUM>, a state reference model establishment module <NUM>, an online monitoring module <NUM> and a man-machine interaction module <NUM>.

The data acquisition module <NUM> collects and saves data of the multi-dimensional states of a device in a process industry.

In practical applications, the data acquisition module <NUM> can collect data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system and save the data, or collect data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and save the data.

The correlation calculation module <NUM> adopts a preset correlation analysis algorithm to perform a correlation analysis of the history data collected by the data acquisition module <NUM> of the multi-dimensional states of the device to obtain the correlated state dimensions.

In practical applications, the apparatus <NUM> can further comprise a man-machine interaction module <NUM>, and the correlation calculation module <NUM> is further used to output the obtained correlated state dimensions to the user through the man-machine interaction module <NUM>, after that, if receiving modification information input by the user about the correlated state dimensions, the man-machine interaction module <NUM> sends the modification information to the correlation calculation module <NUM>, and the correlation calculation module <NUM> modifies the correlated state dimensions according to the modification information.

The state reference model establishment module <NUM> acquires history data of correlated dimensional states from the data acquisition module <NUM> according to the correlated state dimensions obtained by the correlation calculation module <NUM> and uses a preset algorithm for establishing a device state reference model to model the acquired history data of correlated dimensional states to obtain the device state reference model.

In practical applications, the apparatus <NUM> can further comprise a man-machine interaction module <NUM>, and the state reference model establishment module <NUM> is further used to output the obtained device state reference model to the user through the man-machine interaction module <NUM>, after that, if receiving modification information input by the user about the device state reference model, the man-machine interaction module <NUM> sends the modification information to the state reference model establishment module <NUM>, and the state reference model establishment module <NUM> modifies the device state reference model according to the modification information.

The online monitoring module <NUM> acquires real-time data of the corresponding correlated dimensional states of the device state reference model in real time from the data acquisition module <NUM> according to the device state reference model established by the state reference model establishment module <NUM> and determines whether the current state of the device is normal according to a preset device state determination condition, the real-time data of the correlated dimensional states and the device state reference model.

<FIG> is a flowchart of the method executed by the apparatus <NUM> in <FIG> to monitor the state of a device in a process industry in the embodiments of the present invention, and the specific steps are as follows:
Step <NUM>: The data acquisition module <NUM> periodically collects and saves data of the multi-dimensional states of a device in a process industry.

Specifically, the data acquisition module <NUM> can collect data of the multi-dimensional states of a device in a process industry from the following systems:.

The manufacturing data acquisition system collects data of the multi-dimensional states of the device from various sensors and/or various measuring instruments on the device in real time, and then only needs to send the collected data of the multi-dimensional states of the device to the data acquisition module <NUM> in real time, wherein the time sequence data flow collected from each sensor or measuring instrument is used as data of a dimensional state.

For example, for the DCS in the petrochemical industry, the collected data of the multi-dimensional states of a device can contain:.

The SCADA system collects data of the multi-dimensional states of the device from various sensors and/or various measuring instruments on the device in real time, and then only needs to send the collected data of the multi-dimensional states of the device to the data acquisition module <NUM> in real time.

For example, for the SCADA system in the hydropower industry, the collected data of the multi-dimensional states of a device can contain:.

For the device design system, the data collected by the data acquisition module <NUM> of the multi-dimensional states of a device can contain:.

Sometimes, the simulation system can be utilized to simulate the actual production process, and in this case, the data acquisition module <NUM> can directly collect the required data of the multi-dimensional states of a device.

Step <NUM>: The correlation analysis module <NUM> adopts a preset correlation analysis algorithm to perform a correlation analysis of the collected history data of the multi-dimensional states of the device to obtain a correlation indicator vector, wherein the correlation indicator vector indicates the correlated state dimensions and the description of the correlation between the correlated dimensional states.

It should be noted that when a correlation analysis is performed for the collected history data of the multi-dimensional states of the device in this step, the state dimensions can be based on the collected object of the state data, for example, the state data coming from each collected object such as sensor or measuring instrument is used as data of a dimensional state.

The time range of history data can be set by the user and can be a recent month or a recent year, for example.

For example, when the correlation calculation module <NUM> performs a correlation analysis of history data of n (n is an integer greater than <NUM>) dimensional states of the device, the obtained correlation indicator vector indicates that m (m is an integer greater than <NUM> but less than or equal to n) dimensional states are correlated and the correlation indicator vector contains a description of the correlation between m dimensional states.

The description of the correlation between correlated dimensional states can be as follows: A change of a dimensional state or multi-dimensional states will cause a change to another dimensional state or other multi-dimensional states. For example, when a first change happens to the state of dimension a in a first time, a second change will be caused to the state of dimension b in a second time, a third change will be caused to the state of dimension c in a third time,. , wherein the first time, the second time, the third time,. , can be a time point or a time range.

For example, if the state dimensions related to the vibrations in a position of a pump need be analyzed, the correlation calculation module <NUM> performs a correlation analysis of the history data of vibrational state of dimension a collected by the data acquisition module <NUM> from the vibration sensor (sensor A) in the position and the history data of the states collected from other sensors on the pump, and finally learns that the states of corresponding dimensions b, d and f of sensors B, D and F are correlated to the vibrational state of dimension a of sensor A and obtains the description (for example, how long and what change will happen to the data of the states of dimensions b, d and f output from sensors B, D and F after sensor A detects that the vibrational state of dimension a is intensified: e.g. the temperature output from sensor B will rise in <NUM> to <NUM> after sensor A detects that the vibration is intensified) about the correlation between the states of dimensions b, d and f and the state of dimension a.

In practical applications, the user can set a target state dimension for a correlation analysis on the correlation calculation module <NUM>, and the correlation calculation module <NUM> will calculate the dimensions related to the target state dimension according to the collected history data of all state dimensions of the device.

Wherein, the correlation analysis algorithm is, for example, the Pearson correlation algorithm or the Kendall correlation algorithm. The correlation analysis algorithm is not restricted in the present invention and can be preset in advance according to experience and the scenario.

In addition, the user further needs to configure some configuration parameters necessary for a correlation analysis on the correlation calculation module <NUM>, for example, configure the time range (for example, in a recent month or a recent year) of history data of a state, and a correlation determination threshold (for example, when the correlation between two state dimensions is greater than a first threshold, the two state dimensions are determined to be correlated, and otherwise, the two state dimensions are not correlated).

After the correlation calculation module <NUM> obtains a correlation indicator vector, the correlation indicator vector can be output through the man-machine interaction module <NUM>, and the user can modify the correlation indicator vector (for example, delete a part of state dimensions from the correlation indicator vector or add other state dimensions to the correlation indicator vector) according to his/her own judgment, or the user can change the values of a part or all of the configuration parameters of the correlation analysis algorithm through the man-machine interaction module <NUM> and trigger the correlation calculation module <NUM> to perform a correlation analysis again until the user is satisfied with the output correlation indicator vector.

Step <NUM>: The state reference model establishment module <NUM> determines the correlated state dimensions according to the correlation indicator vector generated by the correlation calculation module <NUM>, acquires history data of correlated dimensional states from the data acquisition module <NUM>, and uses a preset algorithm for establishing a device state reference model to model the acquired history data of correlated dimensional states to obtain the device state reference model.

For example, if the correlation indicator vector generated by the correlation calculation module <NUM> indicates that m dimensional states of the device are correlated, the state reference model establishment module <NUM> acquires history data of m dimensional states from the data acquisition module <NUM>. The time range of history data can be set by the user.

The algorithm for establishing a device state reference model is, for example, the Dirichlet process algorithm based on the Gaussian mixture model. The algorithm for establishing a device state reference model is not restricted in the present invention and can be preset in advance according to experience and the scenario.

In addition, the user further needs to configure some configuration parameters necessary for the algorithm for establishing a device state reference model on the state reference model establishment module <NUM>, for example, configure the time range (for example, in a recent month or a recent year) of history data of a state.

Likewise, after the state reference model establishment module <NUM> obtains the device state reference model, the device state reference model can be output to the user through the man-machine interaction module <NUM>, and the user can modify the device state reference model through the man-machine interaction module <NUM>, or the user can change the values of a part or all of the configuration parameters of the algorithm for establishing a device state reference model through the man-machine interaction module <NUM> and trigger the state reference model establishment module <NUM> to re-establish a device state reference model until the user is satisfied with the output device state reference model.

Step <NUM>: The online monitoring module <NUM> acquires real-time data of the corresponding correlated dimensional states of the model from the data acquisition module <NUM> in real time according to the established device state reference model and determines whether the current state of the device is normal according to a preset device state determination condition, the real-time data of the correlated dimensional states and the device state reference model.

Likewise, after the online monitoring module <NUM> obtains the device state monitoring result (normal or abnormal), the device state monitoring result can be output to the user through the man-machine interaction module <NUM>, and the user can modify the device state monitoring result through the man-machine interaction module <NUM> according to experience. If the user modifies the current state of the device according to experience, the corresponding device state reference model can be considered inaccurate, and the real-time data acquired in Step <NUM> of corresponding correlated dimensional states of the device state reference model is used as history data to re-establish a device state reference model.

Considering that the aging of a device will cause the production capacity of the device to change, steps <NUM> to <NUM> can be performed at intervals, for example, once a month, to continuously update the device state reference model, or can be performed at any time according to the requirements of the user.

<FIG> shows the structure of the apparatus <NUM> for monitoring the state of a device in a process industry in another embodiment of the present invention, and the apparatus comprises a processor <NUM> and a memory <NUM>, wherein
applications are stored in the memory <NUM>, which applications can be executed by the processor <NUM> to enable the processor <NUM> to perform the following steps of the method for monitoring the state of a device in a process industry:.

In practical applications, the step performed by the processor <NUM> of collecting and saving data of the multi-dimensional states of a device in a process industry in real time specifically comprises:
collecting data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system and saving the data, or collecting data of the multi-dimensional states of the device in the process industry in real time from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and saving the data.

In practical applications, after obtaining the correlated state dimensions, the processor <NUM> further performs the following step:
output the obtained correlated state dimensions to the user, and if receiving modification information input by the user about the correlated state dimensions, modify the correlated state dimensions according to the modification information.

In practical applications, after obtaining the device state reference model, the processor <NUM> further performs the following step:
output the obtained device state reference model to the user, and if receiving modification information input by the user about the device state reference model, modify the device state reference model according to the modification information.

In practical applications, the correlation analysis algorithm is the Pearson correlation algorithm and/or the Kendall correlation algorithm, and the algorithm for establishing a device state reference model is the Dirichlet process algorithm based on the Gaussian mixture model.

The present invention further provides a computer readable storage medium, the computer readable storage medium stores a computer program and the computer program performs the following steps of the method for monitoring the state of a device in a process industry when executed by a processor:.

In practical applications, the step performed by the computer program of collecting and saving data of the multi-dimensional states of a device in a process industry in real time specifically comprises:
collecting data of the multi-dimensional states of the device in the process industry from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system and saving the data, or collecting data of the multi-dimensional states of the device in the process industry in real time from one or any combination of the manufacturing data acquisition system, the supervisory control and data acquisition system and the simulation system, and the device design system and saving the data.

In practical applications, when executed by a processor, the computer program further performs the following step:
output the obtained correlated state dimensions to the user, and if receiving modification information input by the user about the correlated state dimensions, modify the correlated state dimensions according to the modification information.

In practical applications, when executed by a processor, the computer program further performs the following step:
output the obtained device state reference model to the user, and if receiving modification information input by the user about the device state reference model, modify the device state reference model according to the modification information.

Claim 1:
A method for monitoring the state of a device in a process industry, characterized in that the method comprises:
collecting and saving (S101) data of multi-dimensional states of a device in the process industry;
adopting (S102) a preset correlation analysis algorithm to perform a correlation analysis of the collected data of the multi-dimensional states of the device to obtain correlated dimensional state, wherein a dimensional state is used as a target state and the correlation analysis algorithm is used to analyze which of the dimensional states are correlated to the target state;
acquiring (S103) history data of correlated dimensional states according to the obtained correlated state dimensions;
adopting (S103) a preset algorithm to model the acquired history data of correlated dimensional states to obtain a device state reference model;
acquiring (S104) real-time data of correlated dimensional states of the device state reference model in real time; and
determining (S104) whether a current state of the device is normal wherein, if the difference between a data value of a target state calculated according to the device state reference model from the real-time data and an actual target state data value is less than a preset threshold, the target state is considered normal, otherwise, the target state is abnormal.