CN108871434B

CN108871434B - Online monitoring system and method for rotating equipment

Info

Publication number: CN108871434B
Application number: CN201810541293.2A
Authority: CN
Inventors: 陈得民
Original assignee: Beijing Will Create Technology Co ltd
Current assignee: Beijing Will Create Technology Co ltd
Priority date: 2018-05-30
Filing date: 2018-05-30
Publication date: 2024-05-31
Anticipated expiration: 2038-05-30
Also published as: CN108871434A

Abstract

The invention discloses an online monitoring system and method of rotating equipment, wherein the established online monitoring of the rotating equipment comprises a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, wherein the plurality of wireless fault early warning sensors are configured for each rotating equipment in at least one monitoring area, and different wireless fault early warning sensors send collected monitoring data of different measuring points to the cloud platform through the data gateway, and the cloud platform analyzes and processes the monitoring data. Furthermore, the system also comprises fault diagnosis equipment arranged for the rotating equipment, diagnosis data are obtained after fault diagnosis is carried out on the rotating equipment, and the diagnosis data are sent to the cloud platform for processing through the data gateway. The cloud platform comprises a cloud server, a big data processing module, a fault early warning library, a fault diagnosis library, a management module and the like, can analyze and process all monitoring data, and instructs the fault diagnosis equipment to diagnose the rotating equipment according to analysis results and acquire diagnosis data. Thus, the system and the method provided by the embodiment of the invention realize that a plurality of rotary devices share one fault early warning and diagnosis system, and realize the sharing of fault information and diagnosis modes.

Description

Online monitoring system and method for rotating equipment

Technical Field

The present invention relates to a monitoring technology for a device, and in particular, to an online monitoring system and method for a rotating device.

Background

In the operation process of mechanical equipment, especially in the operation process of rotating equipment, the working state of the rotating equipment needs to be monitored, and once faults are found, fault alarming and subsequent diagnosis and maintenance are carried out. At present, in order to realize fault alarm of rotating equipment, a rotating equipment fault early warning system is arranged for the rotating equipment, and the rotating equipment fault early warning system comprises sensors arranged for all measuring points of the rotating equipment and fault diagnosis equipment interacted with the sensors.

The existing fault early warning system of the rotating equipment has simple functions, is independent, only plays a role in alarming when faults occur, and adopts a special system to test, analyze and remove the faults when the rotating equipment fails; the existing fault early warning system of the rotating equipment does not carry out comparison analysis on the obtained fault detection data, cannot be used as a basis for later fault judgment, and causes waste of monitoring data; the existing fault early warning system of the rotating equipment requires a professional to have high professional knowledge and experience, and many fault diagnosis equipment not only has a data acquisition function, but also has a certain data analysis function, and the equipment cost is extremely high. When a relatively complex fault occurs, a user is required to spend a large amount of money to apply an experienced expert to remove the fault, and after the expert removes the fault, expensive fault diagnosis equipment is required. The existing fault early warning system of the rotary equipment is a single system, the data measured by each set of system are not shared, the fault diagnosis method and the solution method are not shared, the utilization rate of the monitored data is low, and the fault diagnosis cost of the equipment is increased. The fault analysis of the conventional rotary equipment fault early warning system is based on the analysis of measurement data of a single rotary equipment, and the fault type is judged by means of expert experience. The existing fault early warning system of the rotating equipment is not directly related to other fault diagnosis systems, detected data are not shared, and when the fault early warning system gives out fault warning, maintenance personnel carry relevant fault diagnosis equipment to further measure and analyze the rotating equipment with faults.

Disclosure of Invention

The embodiment of the invention provides an online monitoring system of rotary equipment, which can realize that a plurality of rotary equipment share one fault early warning and diagnosis system and realize the sharing of fault information and diagnosis modes.

The embodiment of the invention also provides an online monitoring method of the rotating equipment, which can realize that a plurality of rotating equipment share one fault early warning and diagnosis system and realize the sharing of fault information and diagnosis modes.

The embodiment of the invention is realized as follows:

An online monitoring system for a rotating device, comprising: the system comprises a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, wherein,

The wireless fault early warning sensors are arranged on different measuring points of each rotating device in more than one rotating device, acquire monitoring data of corresponding measuring points and send the monitoring data to the cloud platform through the data gateway;

And the cloud platform is used for receiving the monitoring data corresponding to different measuring points of the rotating equipment through the data gateway, and analyzing and processing the monitoring data to obtain a fault result.

In a monitoring area, wireless fault early-warning sensors and data gateways which are arranged on different measuring points of each rotating device form a wireless fault early-warning sensor network;

in a monitoring mode, the monitoring data are collected according to a set sampling interval and are provided with time stamps;

When the monitoring data exceeds a set first threshold value, a half-fault early warning mode is entered, and the monitoring data is acquired for a time domain signal with a certain time length according to a set sampling interval;

and when the monitoring data exceeds a set second threshold value, entering into a full-fault early warning mode, and immediately sending the monitoring data.

The cloud platform includes: the system comprises a cloud server, a big data processing module, a first equipment archive module and a fault early warning library, wherein,

The cloud server is used for indicating the wireless fault early-warning sensor and the data gateway to form a wireless fault early-warning sensor network;

the first equipment archive module is used for storing monitoring data corresponding to different measuring points of the rotating equipment;

The fault early warning library is used for storing and updating fault results corresponding to the analyzed monitoring data;

And the big data processing module is used for analyzing and processing the monitoring data corresponding to different measuring points of the rotating equipment according to the stored monitoring data, determining a fault result corresponding to the analyzed monitoring data from the fault early warning library, and sending an alarm indication.

Further comprises: the fault diagnosis device is used for carrying out fault diagnosis on the rotating device to obtain diagnosis data, and sending the diagnosis data to the cloud platform for processing through the data gateway;

and the cloud platform is used for analyzing after receiving the diagnosis data to obtain a diagnosis result.

The cloud platform comprises: the system comprises a fault diagnosis library, a second equipment archive module and a data processing and analyzing platform, wherein,

The fault diagnosis library is used for storing and updating diagnosis results corresponding to the analyzed diagnosis data;

a second device archive module for storing diagnostic data;

And the data analysis platform is used for analyzing and processing the stored diagnosis data and determining a corresponding diagnosis result from the fault diagnosis library.

Further comprises: and the management module is used for interacting with the management module in the cloud platform to acquire various data of the rotating equipment in the cloud platform or/and upload various data of the rotating equipment for the cloud platform.

An online monitoring method of rotating equipment, a plurality of wireless fault early warning sensors are arranged in a monitoring area and are positioned at different measuring points of each of more than one rotating equipment, the method comprises the following steps:

the cloud platform receives monitoring data corresponding to different measuring points of the rotating equipment, which are acquired by the wireless fault early warning sensor, through the data gateway;

And the cloud platform analyzes and processes the monitoring data to obtain a fault result.

Also included is a fault diagnosis apparatus, the method further comprising:

diagnosing the fault measuring points of the rotating equipment, and sending the obtained diagnosis data to the cloud platform for processing through the data gateway; and after the cloud platform receives the diagnosis data, obtaining a diagnosis result.

The wireless fault early-warning sensor and the data gateway form a wireless fault early-warning sensor network;

Analyzing and processing the monitoring data to obtain fault results based on fault information stored in a set fault early warning library and updated in real time;

the analysis diagnosis data is based on diagnosis information stored in a fault diagnosis library and updated in real time.

The method further comprises the steps of:

And starting a management module to further analyze the monitoring data or/and the diagnosis information of the rotating equipment.

As can be seen from the above, in online monitoring of the rotating device established in the embodiment of the present invention, the online monitoring of the rotating device includes a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, and a plurality of wireless fault early warning sensors are configured for each rotating device in at least one monitoring area, and different wireless fault early warning sensors send collected monitoring data of different measuring points to the cloud platform through the data gateway, and the cloud platform analyzes and processes the monitoring data. Furthermore, the system also comprises fault diagnosis equipment arranged for the rotating equipment, diagnosis data are obtained after fault diagnosis is carried out on the rotating equipment, and the diagnosis data are sent to the cloud platform for processing through the data gateway. The cloud platform comprises a cloud server, a big data processing module, a fault early warning library, a fault diagnosis library, a management module and the like, can analyze and process all monitoring data, and instructs the fault diagnosis equipment to diagnose the rotating equipment according to analysis results and acquire diagnosis data. Thus, the system and the method provided by the embodiment of the invention realize that a plurality of rotary devices share one fault early warning and diagnosis system, and realize the sharing of fault information and diagnosis modes.

Drawings

Fig. 1 is a schematic structural diagram of an online monitoring system of a rotating device according to an embodiment of the present invention;

Fig. 2 is a schematic diagram of an online monitoring method of a rotating device according to an embodiment of the present invention;

FIGS. 3a, 3b and 3C are graphs of historical data analysis data for physical quantities A, B and C of a Q1 device provided by an embodiment of the present invention;

FIGS. 4a, 4b and 4c are graphs of historical data analysis data of physical quantities C, B and A of three rotating apparatuses at time t1 according to an embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating a half-fault diagnosis and half-fault early warning process according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of an online monitoring method of a rotating device according to an embodiment of the present invention;

Fig. 7 is a schematic structural diagram of a wireless fault early warning sensor network constructed according to an embodiment of the present invention;

FIGS. 8a, 8b, 8c and 8d are schematic diagrams illustrating analysis of the acceleration envelope, the true peak, the effective value and the temperature value of the Q1 rotation device at three measurement points according to an embodiment of the present invention;

FIG. 9 is a diagram showing the time when three rotating devices monitor the monitoring data according to an embodiment of the present invention;

Fig. 10a, 10b, 10c and 10d are schematic diagrams of lateral analysis of temperature data, speed effective value data, acceleration true peak value data and acceleration envelope value data at the same point of three rotating devices at time T1 according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below by referring to the accompanying drawings and examples.

In order to realize sharing of fault early warning and diagnosis systems by a plurality of rotating devices and realize sharing of fault information and diagnosis modes, the embodiment of the invention comprises a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, wherein the wireless fault early warning sensors are configured for each rotating device in at least one monitoring area, and the different wireless fault early warning sensors transmit collected monitoring data of different measuring points to the cloud platform through the data gateway, and the cloud platform analyzes and processes the monitoring data. Furthermore, the system also comprises fault diagnosis equipment arranged for the rotating equipment, diagnosis data are obtained after fault diagnosis is carried out on the rotating equipment, and the diagnosis data are sent to the cloud platform for processing through the data gateway. The cloud platform is provided with a big data processing module, a fault early warning library, a fault diagnosis library, a management module and the like, can analyze and process all monitoring data, and instructs the fault diagnosis equipment to diagnose the rotating equipment according to analysis results and obtain diagnosis data. Thus, the system and the method provided by the embodiment of the invention realize that a plurality of rotary devices share one fault early warning and diagnosis system, and realize the sharing of fault information and diagnosis modes.

Fig. 1 is a schematic structural diagram of an online monitoring system of a rotating device according to an embodiment of the present invention, including: the system comprises a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, wherein,

In the system, the wireless gateway is arranged in a distance range capable of communicating with a plurality of wireless fault early warning sensors.

In the system, in a monitoring area, wireless fault early-warning sensors and data gateways which are arranged on different measuring points of each rotating device form a wireless fault early-warning sensor network.

In this system, the cloud platform includes: the system comprises a cloud server, a big data processing module, a first equipment archive module, a second equipment archive module, a fault early warning library, a fault diagnosis library and the like, wherein received monitoring data are analyzed and processed, and a fault result is obtained through analysis. Specifically, a wireless fault early warning sensor network is mainly established. And (3) storing and analyzing the monitoring data, establishing a fault early warning library, analyzing and judging the monitoring data, giving out fault information including alarm information, fault types and the like, and establishing a fault early warning file of the rotating equipment. Further, the diagnostic data is stored, analyzed and a diagnostic database is built, the diagnostic data is judged, diagnostic results are given, the diagnostic results comprise fault types, solutions and the like, and a fault diagnosis file of the rotating equipment is built. The management module of the cloud platform is mainly used for managing the cloud platform, feeding information of the cloud platform back to related personnel, logging in the module through other terminals such as a computer and a mobile phone, and processing related data of the cloud platform through different management authorities.

In the system, a plurality of wireless fault early warning sensors also have a data synchronous acquisition function, namely, monitoring data are acquired at the same time according to a set sampling interval, and the acquired monitoring data are provided with time stamps.

The wireless fault early warning sensor in the system has the functions of semi-fault early warning and semi-fault diagnosis, namely, in a normal working state, monitoring data are collected according to a set sampling interval, characteristic values are carried out inside the monitoring data, and the monitoring data are sent to the cloud platform through the data gateway according to a set sending interval. In particular, when the obtained monitoring data exceeds the set second threshold value, the monitoring data can be immediately sent to the cloud platform, and the cloud platform immediately processes the monitoring data. And when the obtained monitoring data exceeds a set first threshold value, acquiring time domain signals with a certain time length at a high speed according to a set sampling interval, and sending the time domain signals to the cloud platform through the data gateway.

The cloud platform of the system comprises a corresponding relation between the wireless fault early-warning sensor identification and the collected monitoring data, and the wireless fault early-warning sensor identification is arranged on the wireless fault early-warning sensor in a two-dimensional code representation mode. When any terminal scans the two-dimensional code on the wireless fault early-warning sensor, information interaction is carried out between the terminal and a cloud server of the cloud platform, monitoring data corresponding to the wireless fault early-warning sensor identification are obtained, and the monitoring data comprise: eigenvalues, historical curves, historical faults, and so on.

In the system, a wireless sensor network is formed by the wireless fault early-warning sensors arranged on each rotating device and a data gateway connected with the wireless fault early-warning sensors, in the network, all the wireless fault early-warning sensors are started after being installed, monitoring data can be acquired after the wireless fault early-warning sensors are started, and when the data gateway receives the monitoring data acquired by all the sensors in the network, a synchronous monitoring acquisition command is started, and all the sensors in the network enter a synchronous monitoring mode. When the monitoring data monitored by any one sensor in the network exceeds a set first threshold value, the data gateway starts a synchronous semi-fault acquisition command and synchronously acquires time domain signals.

In the system, the sampling interval and the monitoring data sending interval of the wireless fault early warning sensor are long, the power consumption is low, the disposable dry battery is adopted for power supply, and the service life is more than 3 years.

In the system, the system further comprises fault diagnosis equipment, wherein the fault diagnosis equipment consists of a wired sensor and a high-speed data collector, and the multiple monitoring areas are common. When fault diagnosis is needed, a manager of the management layer carries fault diagnosis equipment, diagnosis is carried out on set measuring points of the rotating equipment, and diagnosis data are sent to the cloud platform through the data gateway.

In the fault diagnosis equipment, the high-speed data acquisition device has a multi-channel data synchronous acquisition function, can be externally connected with various types of high-frequency wired sensors, is powered by an internal rechargeable battery, is provided with a high-capacity memory card for storing acquired data, is communicated with a data gateway in a wired mode or a short-distance wireless mode, and transmits diagnosis data to the cloud platform.

In the system, the data gateway is used for sending monitoring data or/and diagnosis data to the cloud platform, and is composed of a wireless communication module for communicating with a wireless fault early warning sensor, a data processing module for data processing, a wired data interface for receiving the diagnosis data, a wired interface or/and a wireless interface for forwarding the received monitoring data or/and diagnosis data to the cloud platform, and the like, is powered by a wired power supply, and has a Global Positioning System (GPS) positioning function.

On a cloud platform in the system, storing the corresponding relation between a data gateway identifier and a plurality of wireless fault early warning sensor identifiers in a wireless sensor network, and setting the data gateway identifier on the data gateway in a two-dimensional code representation mode. When any terminal scans the two-dimensional code on the data gateway, the terminal performs information interaction with the cloud platform to acquire related information in the wireless sensor network, and the method comprises the following steps: all wireless fault early warning sensor numbers, data gateway positions, rotating equipment types monitored by the data gateway, state information and fault histories connected with the data gateway.

In the system, management staff with different authorities log on the cloud platform through the management module to inquire monitoring data, fault results or/and diagnosis data of the rotating equipment, manage the cloud platform and the like. The system operators mainly comprise fault analysis specialists, rotating equipment operation maintenance staff, rotating equipment factories and system management staff. The fault analysis expert mainly processes and analyzes the monitoring data or the diagnosis data by adopting a relevant consultation fault analysis expert when the fault early warning library in the cloud platform fails to find out the corresponding fault type information when the rotating equipment fails, and sends the processing result to the fault early warning library and the fault diagnosis library of the cloud platform for storage. The operation and maintenance personnel of the rotating equipment mainly maintain the rotating equipment according to fault results in the cloud platform, collect diagnostic data of the rotating equipment by using the fault diagnosis equipment according to the prompt of the cloud platform, send the diagnostic data to the cloud platform through a data gateway, and maintain the rotating equipment according to the fault prompt and fault removal method of the cloud platform. The rotating equipment manufacturer mainly tracks the health state of the rotating equipment produced by the rotating equipment manufacturer through the cloud platform, improves the product performance, simultaneously provides certain rotating equipment design parameter information for system management personnel or fault diagnosis specialists, improves the fault diagnosis precision of the rotating equipment, and the system management personnel is mainly responsible for the normal operation of the whole system, coordinates fault analysis specialists, rotating equipment operation maintenance personnel and the rotating equipment manufacturer to perform fault early warning and fault diagnosis on the rotating equipment, and establishes or updates a fault early warning library of the cloud platform.

Fig. 2 is a schematic diagram of an online monitoring method of a rotating device according to an embodiment of the present invention, where a plurality of wireless fault early warning sensors are disposed in a monitoring area and located at different measuring points of the rotating device, and the method includes:

step 201, the cloud platform receives monitoring data corresponding to different measuring points of the rotating equipment, which are acquired by a wireless fault early warning sensor, through a data gateway;

And 202, analyzing and processing the monitoring data by the cloud platform to obtain a fault result.

In the method, a fault diagnosis apparatus is further included, the method further including:

The following describes embodiments of the present invention in detail

The first step, setting a wireless sensor network

In the distance range of the data gateway, two rotating devices are arranged, n wireless fault early warning sensors are arranged on one rotating device, m wireless fault early warning sensors are arranged on the other rotating device, n and m are natural numbers respectively and are all in the communication range of the data gateway,

The networking method comprises the following steps:

1) Each wireless fault early warning sensor has a unique identifier, after each rotating device is set, the data gateway identifier is scanned through a terminal, a binding function interface between the data gateway and the sensor is entered, all the sensor identifiers set on one rotating device are bound with the data gateway identifier, and meanwhile, a group number is allocated to the network to establish a wireless sensor network. The wireless fault early-warning sensor can be connected with the data gateway after being started, and the data gateway immediately starts a synchronous fault monitoring command after receiving all sensor first packet data in the network, so that the network enters a synchronous monitoring state;

2) When one rotating device is networking, the wireless sensor network of the next rotating device is networking, the adopted data gateway is the same when the wireless sensor network is set, other setting modes are the same as the 1 st) point, and the like.

Further, within the same network, the rotating devices being monitored are of the same type.

Second step, monitoring data processing

The monitoring data monitored by the wireless fault early-warning sensor is forwarded to the cloud platform through the data gateway, the cloud platform stores the received monitoring data, the big data processing module in the cloud platform performs data mining and analysis on the stored monitoring data, the analyzed fault result is stored in the fault early-warning library, and early-warning information and fault types are sent to related personnel through the management module.

The processing mode of the monitoring data specifically comprises the following steps:

(1) And establishing an equipment file, and recording the model number, the key performance parameters, the delivery time, the historical fault information and the like of the rotating equipment in the equipment file.

(2) And the big data processing platform counts the wireless fault early warning sensor of each rotating device according to time, and establishes the following data table.

(3) Based on the data table of point (2), a data curve is established for longitudinal data comparison, such as historical data analysis of a monitored physical signal of each measuring point on the same rotating equipment, such as the historical data analysis data curve of physical quantity A, B and C of the Q1 equipment shown in fig. 3a, 3b and 3C.

(4) Based on the data table of point (2), a data curve of the same monitoring information at the same measuring point of a plurality of devices to be limited is established, and data transverse comparison is carried out, such as historical data analysis data curves of physical quantities C, B and A of three rotating devices at time t1 shown in fig. 4a, 4b and 4 c.

(5) And (5) fault early warning judgment. And (3) on the basis of the data processing of the points (2) - (4), carrying out transverse fault early warning judgment and longitudinal fault early warning judgment.

The judgment method is as follows:

a) According to the factory performance design of the rotary equipment, a certain physical quantity threshold value at an initial single measuring point is preset to be [ P _min,P_max ], and if the data monitored in real time is smaller than P _min or larger than _Pmax, the equipment is faulty, namely longitudinal fault early warning judgment is achieved.

B) By counting the same physical parameters of the same measuring points of the same type of rotary equipment in a monitored network at the same time, setting the arithmetic mean value of all the data of the statistics as Q, setting a proportionality coefficient K _Qmin, K _Qmax,K_Qmin and K _Qmax to be larger than 0 and smaller than 1, setting a certain real-time data to be X, and if X is larger than X (1+K _Qmax) or smaller than X (1-K _Qmin), indicating that the equipment has faults, namely judging the transverse faults of a plurality of rotary equipment measuring points in the network.

Further, as the equipment operates, a great amount of monitoring data is increased, fault early warning and fault diagnosis libraries are enriched, and the threshold value [ P _min,P_max ] and the proportionality coefficients K _Qmin and K _Qmax are continuously optimized, so that the fault judgment accuracy is improved.

The optimization method comprises the following steps: when it is detected that certain monitoring data at a certain measuring point of a certain rotating device exceeds a threshold value, an operation and maintenance person considers that the threshold value is larger or smaller through judgment of fault diagnosis equipment or more experienced experts, and then the threshold value is updated to be a more reasonable value.

Further, the fault determination threshold is classified into different levels, P _min、P_max、K(1+K*K_Qmax)、K(1-K*K_Qmin) is a first level threshold, and w×p _min、W*P_max、W*K(1+K*K_Qmax)、W*K(1-K*K_Qmin) is a second level threshold when a coefficient W is set.

(6) All the monitored data are stored in a database of the cloud platform, and can be inquired in the later period; all the fault information is stored in the fault early warning library.

And a third step of: semi-fault diagnosis

When the monitored data exceeds a first level threshold, the wireless fault early-warning sensor network starts a half fault diagnosis function, and the specific implementation mode is as follows:

a) When the data gateway receives that the monitored data exceeds a first level threshold, judging a wireless fault early-warning sensor network where the monitored data is located, and starting a synchronous half-fault acquisition command;

b) And the wireless fault early warning sensor for starting the half fault acquisition command acquires time domain signals with a certain time length at a certain sampling interval at a high speed and sends the time domain signals to the cloud platform.

Further, the high-speed sampling interval may be increased or decreased by the severity of the fault. Only one case is described here: when the monitored data exceeds a first level threshold, a half fault diagnosis command is started, time domain signals are collected and sent at a high speed every day, and meanwhile, the monitoring mode of the wireless fault early warning sensor network is unchanged.

C) After the time domain signals acquired by the wireless fault early warning sensor are transmitted to the cloud platform, a related fault analysis expert processes and analyzes the time domain signals through management software and a data processing and analyzing module to obtain fault types, a solution and a fault diagnosis method are provided, and the fault types and the solution are stored in a fault early warning library.

Further, when the monitored data of the wireless fault early-warning sensor network exceeds a first threshold value, the half fault diagnosis function is started, and only when the monitoring data monitored by the wireless fault early-warning sensor network does not exceed the first threshold value after the equipment operation and maintenance personnel maintain the rotating equipment according to the system prompt, the half fault diagnosis function is stopped by the wireless fault early-warning sensor network. Returning to the normal fault monitoring mode.

Fourth step: semi-fault early warning processing

After the semi-fault diagnosis, when the monitored data exceeds a set first threshold value, the management module inquires a fault early warning library, and sends fault warning information including the current monitored data size, the fault measuring point, the rotating equipment model number and address where the measuring point is located, the fault type and solving method to the rotating equipment operation and maintenance personnel, and after the rotating equipment operation and maintenance personnel receives the fault warning information, the rotating equipment is maintained according to information prompt. Here, in the first-level fault alarm information, the fault solving method is a fault removing method of the rotating device in the fault early warning library, such as slight looseness of the base, need for fastening, and the like.

As shown in fig. 5, fig. 5 is a schematic diagram illustrating a process of half-fault diagnosis and half-fault early warning according to an embodiment of the present invention.

Fifth step: full fault diagnostic start-up

When the monitored data exceeds a set second threshold value, the management software module inquires a fault early warning library, and sends fault alarm information including the current monitored data size, the fault measuring point, the rotating equipment model number and address where the measuring point is located, the fault type and the solving method to the rotating equipment operation and maintenance personnel, and after the rotating equipment operation and maintenance personnel receives the fault alarm information, the rotating equipment is maintained according to information prompt. Here, in the second level fault alarm information, the fault solving method is a fault diagnosis method of the rotating equipment in the fault early warning library, that is, a fault diagnosis hardware and a test method of monitoring data, for example, a 100KHz high-frequency acceleration sensor is adopted to perform acceleration test on a measuring point exceeding a second threshold value.

Sixth step: fault diagnosis data processing

And the rotating equipment operation and maintenance personnel conduct high-speed data acquisition on the rotating equipment needing fault diagnosis by adopting the fault diagnosis equipment according to the prompt. Diagnostic data acquired by the fault diagnosis equipment are uploaded to the cloud platform through the data gateway, and the cloud platform stores the diagnostic data first.

The data processing method comprises the following steps:

a) In the cloud platform, corresponding diagnosis data processing methods are selected according to different diagnosis data types including temperature, acceleration, strain and the like, diagnosis data are processed, and corresponding fault types and fault solving methods are inquired in a fault diagnosis library according to processing results.

Further, the fault diagnosis library is established according to the experience of industry experts in the early stage.

B) If the corresponding fault type is not inquired in the fault diagnosis library, notifying a system manager through the management software module, and coordinating a fault analysis expert to call the original fault diagnosis data to analyze the monitoring data.

Further, when the fault analysis expert analyzes the data, the information such as the design parameters of the related rotating equipment needs to be queried in the equipment archive, and if not, the information can be directly contacted with the rotating equipment manufacturer through coordination of a system manager.

C) The fault analysis expert gives out a fault analysis method, a processing method of the diagnosis data, a fault type and a fault solving method through analysis of the fault diagnosis data.

D) If the fault analysis expert cannot analyze the fault through the existing fault diagnosis data, the fault analysis expert gives a fault diagnosis method, including a fault diagnosis hardware and a data testing method, and informs the rotating equipment operation and maintenance personnel to collect diagnosis data with related fault diagnosis equipment, and the collected diagnosis data is transmitted to the cloud platform again. Again, the fault analysis expert performs the analysis.

E) After the fault analysis expert processes the fault diagnosis data, the given fault analysis methods, including the data processing method, the fault type, the fault solving method and the fault diagnosis method, including the fault diagnosis hardware and the data testing method are stored in the fault diagnosis library.

Seventh step: fault early warning library and fault diagnosis library circulation optimization

A) And (5) circularly optimizing a fault early warning library.

Before the wireless fault early-warning sensor is arranged, a fault early-warning library in the cloud platform is built by fault analysis experts referring to design parameters of the rotating equipment and self experience of the experts, and in the later period, along with the arrangement of a large number of wireless fault early-warning sensors on the rotating equipment and the acquisition of monitoring data, the cloud platform carries out longitudinal analysis and transverse analysis on the large number of monitoring data, and combines with actual field maintenance result feedback, new knowledge accumulation and old knowledge update are continuously carried out on the fault early-warning library.

B) Fault diagnosis knowledge base loop optimization

Before the fault diagnosis equipment is used, a fault diagnosis library in the cloud platform is built by fault analysis experts referring to design parameters of the rotating equipment and self experience of the experts, and in the later period, as the fault diagnosis equipment collects data of the rotating equipment in a large scale, the fault analysis experts analyze the diagnosis data in a large scale through professional data processing analysis software, so that new knowledge accumulation and old knowledge update are continuously carried out on the fault diagnosis library.

C) Knowledge updating between fault pre-warning library and fault diagnosis library

In the cloud platform, correlation exists between the monitored data and the diagnosis data. The correlation will be more pronounced as the amount of monitored data and the amount of diagnostic data monitored increases with the operation of the system.

In the fault early warning system, when the monitored data exceeds a fault early warning threshold value, and fault information is inquired in a fault diagnosis library through correlation between the monitored data and the diagnosis data when the fault early warning information is not found in the fault early warning library. If the required information is queried, the fault is practically removed, and the fault information is copied in a fault early warning library.

In the fault diagnosis system, fault information is obtained through analysis and processing of diagnosis data, and after the rotating equipment operation and maintenance personnel remove the fault according to the fault information, the fault information obtained after diagnosis is copied into a fault early warning library according to the correlation of the fault early warning information and the fault diagnosis information.

Eighth step: operation of management module by system operator

In the system, system operators comprise fault analysis specialists, rotating equipment operation maintenance personnel, rotating equipment factories and system management personnel. And operating the cloud platform by a system operator through system management software.

The management module may be run on a computer, a handheld terminal or a mobile phone.

After receiving the information of the system manager, the fault analysis expert enters a data processing platform with open authority for processing and analyzing the data;

When the rotating equipment fails, the cloud platform automatically alarms to operation and maintenance personnel of the rotating equipment through the management module, and the rotating equipment performs fault maintenance or fault diagnosis on the rotating equipment according to the fault alarm information;

Different rotary equipment manufacturers have different management module use authorities, and the rotary equipment manufacturers track the rotary equipment produced by the rotary equipment manufacturers through the management module and cooperate with rotary equipment operation and maintenance personnel and fault analysis specialists to carry out fault diagnosis and elimination of the rotary equipment.

The system manager is responsible for the operation of the whole system and coordinates the information communication among the system operators.

Fig. 6 is a schematic diagram of an online monitoring method of a rotating device according to an embodiment of the present invention, which specifically includes the following steps:

step 1, establishing a wireless fault early warning sensor network;

step 2, monitoring data analysis, wherein when the monitoring data are normal, the monitoring data are directly output;

step 3, judging the monitoring data, and executing step 4 when the judgment exceeds a first threshold value; when the second threshold value is judged to be exceeded, executing the step 8;

step 4, starting a half fault diagnosis function;

step 5, performing semi-fault diagnosis processing based on a fault early warning library and a fault diagnosis library, wherein the fault early warning library and the fault diagnosis library can be updated;

Step 6, judging whether faults are obtained through recognition, and if yes, outputting fault results; otherwise, executing the step 7;

step 7, starting manual expert data analysis;

Step 8, starting a full fault diagnosis function and carrying out alarm prompt;

step 9, performing full fault diagnosis processing based on the fault early warning library and the fault diagnosis library, wherein the fault early warning library and the fault diagnosis library can be updated;

step 10, judging whether faults are obtained through recognition, and if yes, outputting fault results; otherwise, step 7 is performed.

In the diagram, a result description is output

Outputting a result 1:

description: the rotating equipment is in a normal running state, the system monitors the real-time state data of the rotating equipment, and outputs state information, namely a result 1.

The rotating equipment is in a normal running state, the system monitors the real-time state data of the rotating equipment, and outputs state information, namely a result 1.

Outputting a result 2:

Description: the monitoring data exceeds a first threshold value, the system starts a half-fault diagnosis function, meanwhile, the system inquires fault information in a fault early-warning library, and outputs equipment state information, a half-fault diagnosis data analysis result and a half-fault diagnosis result, namely an output result 2, and if the half-fault diagnosis result is not inquired in the fault early-warning library, the half-fault diagnosis result information is not displayed in the output result. Only the current monitored data is displayed. And automatically prompting a manual expert to analyze the data, and recording the result in a fault early warning and fault diagnosis library.

In general, in the early operation stage of the monitoring system, failure diagnosis experience is not accumulated enough, failure types cannot be easily inquired in a failure knowledge base, but with continuous accumulation of failure experience, a failure early warning base accumulates a large amount of failure information of rotating equipment covered by the monitoring system, and when equipment state information exceeds a threshold value, the failure types are easily inquired.

Outputting a result 3:

Description: and if the monitoring data exceeds a second threshold value, the system semi-fault diagnosis function still operates, meanwhile, the system queries a fault knowledge base, if the fault type can be queried, the result 3 is directly output, and if the semi-fault diagnosis result cannot be queried in the fault early warning base, the full-fault diagnosis result information is not displayed in the output result. Only the current monitoring data, the semi-fault diagnosis data set and the semi-fault diagnosis result are displayed. And prompting a manual expert to analyze the data, and recording the result in a fault early warning and fault diagnosis library.

The method provided by the embodiment of the invention is described in detail by taking a specific example

In this embodiment, 3 rotating devices of the same type are selected for testing, and if there are different types or more of rotating devices, the monitoring and diagnostic methods are as in this embodiment.

In this embodiment, 3 wireless fault early warning sensors are disposed on each rotating device, and a total of 9 wireless fault early warning sensors are disposed on each of the 3 rotating devices.

In this embodiment, the wireless communication distance between the data gateway and the wireless fault pre-established sensor is 300 meters (m), the distances between 3 rotating devices in a factory are relatively close, and within 100m, the 9 wireless fault pre-established sensors arranged on the three rotating devices can forward data by using the same data gateway.

In this embodiment, 9 wireless fault early-warning sensors are disposed on 3 rotating devices, and Q1, Q2 and Q3 respectively indicate 3 rotating devices, where the 9 wireless fault early-warning sensors on the 3 rotating devices and the data gateway form a wireless fault early-warning sensor network.

In this embodiment, the wireless fault early warning sensor may monitor monitoring data such as an acceleration envelope value, an acceleration true peak value, a speed effective value, and a temperature at a measurement point of the rotating device.

In this embodiment, the wireless fault early warning sensor network is established as follows:

As shown in fig. 7, fig. 7 is a schematic structural diagram of a wireless fault early warning sensor network constructed according to an embodiment of the present invention. Wireless fault early warning sensors numbered 1, 2 and 3 are arranged on the Q1 device; wireless fault pre-warning sensors numbered 4, 5 and 6 are arranged on the Q2 device, and wireless fault pre-warning sensor nodes numbered 7, 8 and 9 are arranged on the Q3 device. After arrangement, the system is started, and the nodes are automatically connected with the data gateway.

And scanning the two-dimension code of the data gateway by using the terminal, entering a management software module of the cloud platform, and modifying the node network group number with the number of 1-9 into W1. After grouping, the data gateway sends reset information to the wireless fault sensor through the terminal, and after the wireless fault early-warning sensor is reset, grouping is automatically achieved.

The data packet of the wireless fault early-warning sensor node comprises a sensor number and a network group number, and when leaving a factory, the group number defaults to A.

The wireless fault early warning sensor network after grouping samples monitoring data according to fixed every 20 minutes, and sends the monitoring data every 4 hours.

And when the first threshold value set in the wireless fault early-warning sensor is exceeded, immediately sending the currently monitored monitoring data.

And a second step of: statistical processing of monitored data

Through implementation of the first step, the monitoring data received by the data gateway are sent to the cloud platform, and the cloud platform stores the monitored monitoring data.

The specific treatment method comprises the following steps:

(1) And establishing a device file, wherein the device file 1 records the model number, the key performance parameters, the delivery time and the historical fault information of the rotating device.

(2) And the big data processing module in the cloud platform counts the wireless node data of each rotating device according to time, and establishes the following data table.

(3) According to the data table, a data curve is established, and data longitudinal comparison is performed, namely, historical data analysis of monitoring physical information of each measuring point on each rotating device is performed, and in the embodiment shown in fig. 8a, 8b, 8c and 8d, analysis diagrams of the acceleration envelope value, the acceleration true peak value, the acceleration effective value and the temperature value of the Q1 rotating device on three measuring points are shown.

Correspondingly, the historical data curve of the Q2 rotating equipment and the Q3 rotating equipment at the three measuring points is consistent with the historical data curve establishing method of the Q1 rotating equipment at the three measuring points.

(4) And establishing a data curve of the same monitoring data at the same measuring point of the 3 rotating devices, and transversely mining the monitoring data.

Description: lateral contrast analysis refers to a comparison analysis of a large number of data with similar monitoring.

If the Q1, Q2 and Q3 fault devices are operated for M days, the data of the most-segment moment relative to the moment T1 is transversely acquired by the data at the same measuring point of the three rotating devices on the M th day T1.

In this embodiment, M is 386 days, that is, three rotating devices have been operated for 386 days from startup until the current packet of data is monitored by the three rotating devices; the schedule of monitoring data for three rotating devices is shown in fig. 9.

And at the time of T1, the monitoring data of the three-rotation equipment and the measuring point and the physical quantity are transversely compared, and the monitoring data of the three-rotation equipment are selected according to the principle of nearby: q1 rotating device selects 4:00 data, Q2 rotating device selects 2:00 or 5:00, and the Q3 rotation device selects 3:00 data.

By the selected monitoring data, a schematic diagram of transverse analysis of temperature data, speed effective value data, acceleration true peak value data and acceleration envelope value data at the same measuring point of three rotating devices at the moment T1 in the specific embodiment shown in fig. 10a, 10b, 10c and 10d is established.

The other time data curves at the three measuring points of the three rotating devices are consistent with the transverse data curve establishment method at the three measuring points of fig. 10a, 10b, 10c and 10 d.

And a third step of: fault early warning judgment

And on the basis of the statistical processing of the data, carrying out transverse fault early warning judgment and longitudinal fault early warning judgment. The method comprises the following steps:

(1) Longitudinal fault early warning judgment

And setting a first threshold and a second threshold according to judging standards such as vibration of international or national rotating equipment.

When the monitoring data monitored in real time exceeds a first threshold value, the state of health of the rotating equipment is indicated to be weakly damaged, but still is in an allowable range, and the wireless fault early-warning sensor network starts a half fault diagnosis function.

When the monitored data monitored in real time exceeds the second threshold value, the rotating equipment health state is indicated to have certain damage, but the rotating equipment health state is still in a tolerable range, and the faults need to be diagnosed and removed in time. The system starts the full fault diagnosis function.

(2) Transverse fault early warning judgment

According to the transverse data analysis and statistics method in the second step, data analysis is carried out on the same physical information at the same measuring point of the same rotating equipment according to a certain time interval, and the average value of all data arithmetic of statistics is set as Q.

Given a scaling factor K _Qmin and K _Qmax, a certain monitored data is P, if P is greater than P (1+p×k _Qmax) or less than P (1-p×k _Qmin), then this indicates that the rotating device is faulty. The wireless fault early warning sensor network starts a half fault diagnosis function.

Given a scaling factor L _Qmin and L _Qmax, a certain data monitored by the device is P, if P is greater than P (1+p×l _Qmax) or less than P (1-p×l _Qmin), it indicates that the rotating device is faulty. The wireless fault early warning sensor network starts a full fault diagnosis function.

Further, K _Qmin、K_Qmax、L_Qmin、L_Qmax is greater than 0 and less than 1, and:

K_Qmin<K_Qmax;L_Qmin<L_Qmax;K_Qmin<L_Qmin;K_Qmax<L_Qmax.

In this embodiment, K _Qmin＝0.1、K_Qmax＝0.08、L_Qmin＝0.2、L_Qmax =0.15.

Further, if the average value of the transverse monitoring data sample at a certain moment is Q, when the data at a certain measuring point exceeds the upper limit of the average value Q by 8% or is lower than the average value Q by 10%, the device exceeds a first fault threshold value, and the current wireless fault early-warning sensor starts a half fault diagnosis function; when the data at a certain measuring point exceeds the upper limit of the sample mean value Q by 15% or is lower than the sample mean value Q by 20%, the device exceeds a second fault threshold value, and the current system starts a full fault diagnosis function.

All the monitored data are stored in a database of the cloud platform, and can be inquired in the later period; all the fault information is stored in the fault early warning library.

Fourth step: semi-fault diagnosis function start-up

And when the monitored data exceeds a first threshold value, the wireless fault early-warning sensor network starts a half fault diagnosis function. The specific implementation mode is as follows:

a) When the data gateway receives that certain monitoring data exceeds a first threshold value, judging a wireless fault early-warning sensor network where the monitoring data is located, and starting a half-fault acquisition command;

b) And a wireless fault early warning sensor for starting a half fault acquisition command acquires acceleration time domain signals with a certain time length of about 3s at a high speed according to a sampling interval of 5KHz once a day and sends the acceleration time domain signals to the cloud platform.

Further, the high-speed sampling interval may be increased or decreased by the severity of the fault. Only one case is described here: when the monitored data exceeds a first threshold value, a half fault diagnosis command is started, a time threshold signal is acquired at a high speed every day and sent, and meanwhile, the monitoring mode of the wireless fault early-warning sensor network is unchanged. If the fault is relatively large, but still within acceptable range, acceleration time domain signals can be acquired at high speed every half day and sent to the cloud platform.

C) After the time domain signals acquired by the wireless fault early warning sensor are transmitted to the cloud platform, a related fault analysis expert processes and analyzes the time domain signals through a management module and data processing analysis software to obtain fault types, a solution and a fault diagnosis method are provided, and the fault types and the solution are stored in a fault early warning library.

If the vibration envelope value of one rotating device is monitored to exceed the first threshold value, the wireless fault early warning sensor network system of the rotating device starts a half fault diagnosis function, 3s acceleration time domain signals are collected 1 time per day according to the sampling rate of 10Kz each time, and the collected acceleration time domain signals are sent to the cloud platform.

The cloud platform performs FFT analysis on acceleration time domain signals of semi-fault diagnosis, compares design parameters of rotating equipment, finds out that fundamental frequency is large, queries a fault early warning library, and finds out that the type of fault with the excessive fundamental frequency is unbalanced or thermal bending of a rotating mechanical shaft. The fault diagnosis data processing method and the fault information are stored in a fault diagnosis library.

Fifth step: full fault diagnostic system start-up

In the above process, when the monitored data exceeds the second threshold, the full fault diagnosis function is started.

(1) The system management software polls a fault early warning library, sends fault alarm information including the current monitoring data size, the fault measuring point, the type and address of the rotating equipment where the measuring point is located, the fault type and the fault diagnosis method to the rotating equipment operation and maintenance personnel, and carries out fault diagnosis of the rotating equipment according to information prompt after the rotating equipment operation and maintenance personnel receives the fault alarm information.

Further, the full fault diagnosis method in the fault alarm information of the second level is a diagnosis method of faults of the rotating equipment accumulated in the alarm fault library, namely a fault diagnosis hardware and data testing method, such as multi-measuring-point multi-directional acceleration testing of the rotating equipment by adopting a high-frequency acceleration acquisition system of a 100KHz channel of 4 channels.

(2) And the rotating equipment operation and maintenance personnel take the fault diagnosis equipment to collect high-speed data of the rotating equipment needing fault diagnosis according to the prompt of the system management software. The data collected by the fault diagnosis equipment are uploaded to the cloud platform through the data gateway, and the cloud platform stores the diagnosed data.

The data processing method comprises the following steps:

f) In the cloud platform, corresponding monitoring data processing methods are selected according to different data types including temperature, acceleration, strain and the like, diagnosis data are processed, and corresponding fault types and fault solving methods are inquired in a fault diagnosis library according to processing results.

In this embodiment, the data tested by the fault diagnosis is an acceleration signal, and FFT, modal analysis, etc. may be performed on the acceleration signal.

G) If the corresponding fault type is not inquired in the fault diagnosis library, notifying a system manager through system management software, and coordinating a fault analysis expert to call original fault diagnosis data to analyze monitoring data.

H) The fault analysis expert gives out a fault analysis method, a data processing method, a fault type and a fault solving method through analyzing the fault diagnosis data.

I) If the fault analysis expert cannot analyze the fault by the existing fault diagnosis data, the fault analysis expert gives a fault diagnosis method, which in this embodiment is: and (3) carrying out acceleration tests in three directions at the bearing seat and acceleration tests in the vertical direction at the base by adopting a high-frequency acceleration acquisition system of a 100KHz channel of the 4 channels for rotating equipment exceeding a second threshold, informing the rotating equipment operation and maintenance personnel of carrying out data acquisition with related fault diagnosis equipment, and transmitting the acquired data to the cloud platform again. Again, the analysis is performed by the fault analysis expert.

J) After the fault diagnosis data are processed by the fault analysis expert, a fault analysis method is provided. In this embodiment, the fault analysis method is to perform FFT analysis and modal analysis, fault type, fault resolution method, and fault diagnosis method on the collected acceleration values. The fault diagnosis method comprises the following steps: and (3) carrying out acceleration tests in three directions at the bearing seat and acceleration tests in the vertical direction at the base on the rotating equipment exceeding the second level threshold by adopting a high-frequency acceleration acquisition system of a 100KHz channel of 4 channels, and storing the acceleration tests in a fault diagnosis library.

Sixth step: fault early warning and handling

In the above process, when the monitored value exceeds the first threshold value, the system management software automatically queries the fault early warning knowledge base, sends the fault alarm information including the current monitored data size, the fault measuring point, the type and address of the rotating equipment where the measuring point is located, the fault type and the solving method to the rotating equipment operation and maintenance personnel, and after the rotating equipment operation and maintenance personnel receives the fault alarm information, carries out maintenance of the rotating equipment according to the information prompt.

And meanwhile, starting the semi-fault diagnosis function in the third step, and updating a fault early warning library and a fault diagnosis library.

Further, in the first-level fault alarm information, the fault solving method is a method for removing faults of the rotating equipment accumulated in the fault early warning library, such as loosening of a machine seat, fastening requirement and the like.

A) And (5) circularly optimizing a fault early warning library.

Before the wireless fault early-warning sensor is arranged, a fault early-warning library in the cloud platform is built by fault analysis experts referring to design parameters of the rotating equipment and self experience of the experts, and in the later period, along with the arrangement of a large number of wireless fault early-warning sensors on the rotating equipment and the acquisition of monitoring data, the cloud platform carries out longitudinal analysis and transverse analysis on the monitoring data monitored in a large number, and combines actual field maintenance result feedback to continuously carry out new knowledge accumulation and old knowledge update on the fault early-warning library.

B) Fault diagnosis library cycle optimization

In the cloud platform, correlation exists between the monitoring data and the fault diagnosis data. The correlation will be more pronounced as the amount of monitored data and the amount of diagnostic data monitored increases with the operation of the system.

In the fault diagnosis system, fault information is obtained through analysis and processing of diagnosis data, and after the rotating equipment operation and maintenance personnel remove the fault according to the fault information, the fault information obtained after diagnosis is copied into a fault early warning knowledge base according to the correlation of the fault early warning information and the fault diagnosis information.

Eighth step: operation of system management software by system operator

The system management software can be run on computers, hand-held terminals and cell phones.

When the rotating equipment fails, the cloud platform automatically alarms to operation and maintenance personnel of the rotating equipment through system management software, and the rotating equipment performs fault maintenance or fault diagnosis on the rotating equipment according to the fault alarm information;

Different rotary equipment manufacturers have different system management software use authorities, and the rotary equipment manufacturers track the rotary equipment produced by themselves through the system management software and cooperate with rotary equipment operation maintenance personnel and fault analysis specialists to diagnose and remove faults of the rotary equipment.

The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.

Claims

1. An online monitoring system for a rotating device, comprising: the system comprises a plurality of wireless fault early warning sensors, a data gateway and a cloud platform, wherein,

The cloud platform is used for receiving the monitoring data corresponding to different measuring points of the rotating equipment through the data gateway, and analyzing and processing the monitoring data to obtain a fault result;

When the monitoring data exceeds a set second threshold value, entering into a full-fault early warning mode, and immediately sending the monitoring data;

The system further comprises: the fault diagnosis device is used for carrying out fault diagnosis on the rotating device to obtain diagnosis data, and sending the diagnosis data to the cloud platform for processing through the data gateway;

the cloud platform is used for analyzing after receiving the diagnosis data to obtain a diagnosis result;

The fault early warning comprises transverse fault early warning and longitudinal fault early warning;

The longitudinal fault early warning is as follows:

presetting a certain physical quantity threshold value at an initial single measuring point as [ P _min,P_max ], and if the data monitored in real time is smaller than P _min or larger than P _max, indicating that the equipment has faults, namely, carrying out longitudinal fault early warning judgment;

the transverse fault early warning is as follows:

By counting the same physical parameters of the same measuring points of the same type of rotating equipment in a monitored network at the same time, setting all data arithmetic mean values of the statistics as Q, setting a proportionality coefficient K _Qmin, K _Qmax,K_Qmin and K _Qmax to be larger than 0 and smaller than 1, setting a certain real-time data to be X, and if X is larger than Q (1+K _Qmax) or smaller than Q (1-K _Qmin), indicating that the equipment has faults, namely judging the transverse faults of a plurality of rotating equipment measuring points in the network;

When the monitored data exceeds a first threshold value, a management module of the cloud platform inquires a fault early warning library and sends fault warning information to operation and maintenance personnel of the rotary equipment;

and when the monitored data exceeds a set second threshold value, the management software module of the cloud platform queries a fault early warning library and sends fault warning information to the operation and maintenance personnel of the rotating equipment.

2. The monitoring system of claim 1, wherein the cloud platform comprises: the system comprises a cloud server, a big data processing module, a first equipment archive module and a fault early warning library, wherein,

3. The monitoring system of claim 1, wherein the cloud platform comprises: the system comprises a fault diagnosis library, a second equipment archive module and a data processing and analyzing platform, wherein,

a second device archive module for storing diagnostic data;

4. The monitoring system of claim 1 or 2, further comprising: and the management module is used for interacting with the management module in the cloud platform to acquire various data of the rotating equipment in the cloud platform or/and upload various data of the rotating equipment for the cloud platform.

5. An online monitoring method for rotating equipment is characterized in that a plurality of wireless fault early warning sensors are arranged in a monitoring area and positioned at different measuring points of each of more than one rotating equipment, and the method comprises the following steps:

The cloud platform analyzes and processes the monitoring data to obtain a fault result;

also included is a fault diagnosis apparatus, the method further comprising:

Diagnosing the fault measuring points of the rotating equipment, and sending the obtained diagnosis data to the cloud platform for processing through the data gateway; after the cloud platform receives the diagnosis data, the diagnosis result is obtained;

The longitudinal fault early warning is as follows:

the transverse fault early warning is as follows:

6. The method of claim 5, wherein the analyzing the monitoring data to obtain the fault result is based on fault information stored in a set fault early warning library and updated in real time;

7. The method of claim 5, wherein the method further comprises: