Multi-sensor detection method and platform based on Internet of Things

Abstract

The embodiment of the present invention provides a multi-sensor detection method and platform based on the Internet of Things, which belongs to the field of detection and processing technology. The multi-sensor detection platform based on the Internet of Things includes a sensor group, a communication module group and an edge computing device; the sensor group and the edge computing device are communicatively connected through the communication module group. The sensor group is used to collect the first vibration data and the first gas concentration data of the target Internet of Things terminal device and upload them to the edge computing device through the communication module group; the edge computing device is used to receive the first vibration data and the first gas concentration data, extract the first vibration feature from the first vibration data, and extract the first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtain the fault prediction result of the target Internet of Things terminal device; output a fault diagnosis report based on the fault prediction result of the target Internet of Things terminal device.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of detection technology, and in particular to a multi-sensor detection method and platform based on the Internet of Things. Background Art

[0002] With the advancement of industrial automation and intelligence, equipment reliability and maintenance efficiency have become key concerns for companies. Traditional equipment maintenance relies primarily on regular manual inspections and post-event repairs. This approach has numerous drawbacks, including high maintenance costs, slow response times, and difficulty predicting potential failures. Therefore, achieving real-time equipment monitoring and fault prediction through advanced technologies has become a key research area.

[0003] Existing equipment failure prediction methods primarily rely on learning the normal and failure modes of equipment from large amounts of data to achieve fault prediction. However, existing fault prediction models often rely on a single data source and simple feature extraction methods, resulting in low prediction accuracy. Summary of the Invention

[0004] Embodiments of the present invention provide a multi-sensor detection method and platform based on the Internet of Things, which can achieve comprehensive detection and accurate fault prediction of target IoT terminal devices by combining multiple sensor data and edge computing technology.

[0005] In a first aspect, an embodiment of the present invention provides a multi-sensor detection platform based on the Internet of Things, comprising a sensor group, a communication module group, and an edge computing device; the sensor group and the edge computing device are communicatively connected via the communication module group;

[0006] The sensor group is used to collect first vibration data and first gas concentration data of a target IoT terminal device, and upload the first vibration data and the first gas concentration data to the edge computing device through the communication module group;

[0007] The edge computing device is used to receive the first vibration data and the first gas concentration data, extract a first vibration feature from the first vibration data, and extract a first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result of the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result of the target IoT terminal device.

[0008] In a possible design of the first aspect, the edge computing device extracts a first vibration feature from the first vibration data and a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device, specifically for:

[0009] removing high-frequency noise from the first vibration data using a low-pass filter, smoothing the first vibration data from which the high-frequency noise has been removed using a moving average method, and normalizing the smoothed first vibration data;

[0010] extracting first vibration data time domain features and first vibration data frequency domain features from the normalized first vibration data, wherein the first vibration data time domain features include mean, variance, peak, kurtosis, and skewness; and the first vibration data frequency domain features include spectrum and power spectrum density;

[0011] using a high-pass filter to remove low-frequency drift in the first gas concentration data, smoothing the first gas concentration data after removing the low-frequency drift using an exponential smoothing method, and normalizing the smoothed first gas concentration data;

[0012] extracting the first gas concentration features from the normalized first gas concentration data, where the first gas concentration features include a concentration change rate, a gas concentration peak value, and a gas concentration change trend;

[0013] combining the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature into a first multimodal feature vector;

[0014] The first multimodal feature vector is input into the device fault prediction model to obtain a fault prediction result of the target IoT terminal device.

[0015] In a possible design of the first aspect, when the sampling frequencies of the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature are different, the edge computing device is further configured to:

[0016] The first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature are aligned on the same time axis.

[0017] In a possible design of the first aspect, when the edge computing module combines the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature into a first multimodal feature vector, it is specifically configured to:

[0018] Sequentially concatenating the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature into the first multimodal feature vector; or

[0019] Using a feature selection model, the importance of each feature among the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature is evaluated, and the least important features are gradually deleted until the number of remaining features reaches a preset number, and the remaining preset number of features are spliced ​​into the first multimodal feature vector; the first multimodal feature vector includes the most relevant features extracted from the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature.

[0020] In a possible design of the first aspect, the platform further includes: a cloud server;

[0021] The cloud server is used to collect historical vibration data and historical gas concentration data of the target IoT terminal device from the edge computing device, wherein the historical vibration data and the historical gas concentration data include data of the target IoT terminal device in a normal state and data in a faulty state, and use the collected historical vibration data and historical gas concentration data to train the equipment fault prediction model to be trained, and send the trained equipment fault prediction model to the edge computing device.

[0022] In a possible design of the first aspect, when training the to-be-trained equipment fault prediction model, the cloud server is specifically configured to:

[0023] Determining whether the sample data has a fault according to a preset time window to generate labels for the historical vibration data and the historical gas concentration data; wherein each preset time window corresponds to one sample data, and each sample data includes the historical vibration data and the historical gas concentration data;

[0024] Integrating the labeled sample data into a training data set, wherein each sample data in the training data set includes the historical vibration data, the historical gas concentration data, and a corresponding label;

[0025] Dividing the training data set into a training set and a test set;

[0026] Using the sample data in the training set to train the constructed basic fault prediction model to obtain the trained basic fault prediction model;

[0027] Using the trained basic fault prediction model to predict the data in the test set, and calculating the performance index of the trained basic fault prediction model according to the prediction results;

[0028] Generating an ROC curve based on the calculated performance index to evaluate the performance of the trained basic fault prediction model;

[0029] When the performance evaluation of the trained basic fault prediction model is qualified, the equipment fault prediction model is obtained.

[0030] In a possible design of the first aspect, the target IoT terminal device includes a generator and a transformer;

[0031] The sensor group includes a first vibration sensor provided on the generator rotor, a second vibration sensor provided on the generator stator, a third vibration sensor provided on the transformer core, a fourth vibration sensor provided on the transformer winding, a first gas sensor provided around the generator, and a second gas sensor provided in the transformer oil;

[0032] The communication module group includes a first communication module integrated in the first vibration sensor, a second communication module integrated in the second vibration sensor, a third communication module integrated in the third vibration sensor, a fourth communication module integrated in the fourth vibration sensor, a fifth communication module integrated in the first gas sensor, and a sixth communication module integrated in the second gas sensor.

[0033] In a second aspect, an embodiment of the present invention provides a multi-sensor detection method based on the Internet of Things, which is applied to the multi-sensor detection platform based on the Internet of Things as described in the first aspect of the embodiment of the present invention, and the method includes:

[0034] The sensor group collects first vibration data and first gas concentration data of the target IoT terminal device;

[0035] The sensor group uploads the first vibration data and the first gas concentration data to the edge computing device through the communication module group;

[0036] The edge computing device receives the first vibration data and the first gas concentration data, extracts a first vibration feature from the first vibration data, extracts a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device;

[0037] The edge computing device outputs a fault diagnosis report based on the fault prediction result of the target IoT terminal device.

[0038] In a possible design of the first aspect, the edge computing device extracts a first vibration feature from the first vibration data and a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device, including:

[0039] The edge computing device uses a low-pass filter to remove high-frequency noise from the first vibration data, uses a moving average method to smooth the first vibration data after the high-frequency noise is removed, and normalizes the smoothed first vibration data;

[0040] The edge computing device extracts first vibration data time domain features and first vibration data frequency domain features from the normalized first vibration data, where the first vibration data time domain features include mean, variance, peak, kurtosis, and skewness; and the first vibration data frequency domain features include spectrum and power spectrum density.

[0041] The edge computing device uses a high-pass filter to remove low-frequency drift in the first gas concentration data, uses an exponential smoothing method to smooth the first gas concentration data after the low-frequency drift is removed, and normalizes the smoothed first gas concentration data;

[0042] The edge computing device extracts the first gas concentration feature from the normalized first gas concentration data, where the first gas concentration feature includes a concentration change rate, a gas concentration peak value, and a gas concentration change trend;

[0043] The edge computing device combines the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature into a first multimodal feature vector;

[0044] The edge computing device inputs the first multimodal feature vector into the device fault prediction model to obtain a fault prediction result of the target IoT terminal device.

[0045] In a possible design of the second aspect, the method further includes:

[0046] When the sampling frequencies of the first vibration data time domain feature, the first vibration feature frequency domain feature and the first gas concentration feature are different, the edge computing device aligns the first vibration data time domain feature, the first vibration feature frequency domain feature and the first gas concentration feature on the same time axis.

[0047] An embodiment of the present invention provides a multi-sensor detection method and platform based on the Internet of Things. The detection platform includes a sensor group, a communication module group, and an edge computing device. The sensor group and the edge computing device are communicatively connected via the communication module group. The sensor group is used to collect first vibration data and first gas concentration data from a target IoT terminal device and upload them to the edge computing device via the communication module group. The edge computing device is used to receive the first vibration data and first gas concentration data, extract a first vibration feature from the first vibration data, and extract a first gas concentration feature from the first gas concentration data. The first vibration feature and the first gas concentration feature are input into a pre-trained device fault prediction model to obtain a fault prediction result for the target IoT terminal device. A fault diagnosis report is then output based on the fault prediction result for the target IoT terminal device. Thus, the first vibration data and first gas concentration data of the target IoT terminal device are collected in real time by the sensor group and uploaded to the edge computing device. The edge computing device then uses the device fault prediction model obtained from a cloud server to perform a fault prediction based on the first vibration data and the first gas concentration data, thereby promptly determining whether the target IoT terminal device is faulty or about to fail. The fault diagnosis report is then generated to guide maintenance personnel in performing timely maintenance and repairs, thereby achieving comprehensive detection and accurate fault prediction for the target IoT terminal device. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the description, serve to explain the principles of the embodiments of the present invention.

[0049] Figure 1 A schematic diagram of the structure of a multi-sensor detection platform based on the Internet of Things provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the structure of another multi-sensor detection platform based on the Internet of Things provided by an embodiment of the present invention;

[0051] Figure 3 A flowchart of a multi-sensor detection method based on the Internet of Things provided by an embodiment of the present invention is shown;

[0052] Figure 4 A flowchart illustrating a training process of a device failure prediction model provided by an embodiment of the present invention is shown;

[0053] Figure 5 FIG. 1 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention.

[0054] The above drawings illustrate specific embodiments of the present invention, which will be described in more detail below. These drawings and the accompanying description are not intended to limit the scope of the present invention in any way, but rather to illustrate the concepts of the present invention for those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0057] The following describes some embodiments of the present invention in detail with reference to the accompanying drawings. The following embodiments and features thereof may be combined with one another unless they conflict with each other. Furthermore, the sequence of steps in the following method embodiments is provided for illustrative purposes only and is not intended to be a strict limitation.

[0058] Power transmission equipment is diverse and requires frequent inspection to ensure its proper operation. In practice, this equipment includes transformers, icing monitoring devices, and transmission line monitoring devices. With the advancement of industrial automation and intelligent systems, equipment reliability and maintenance efficiency have become key concerns for businesses. Traditional equipment maintenance relies primarily on regular manual inspections and post-event repairs, which have numerous drawbacks, including high maintenance costs, slow fault response, and difficulty predicting potential failures. Therefore, achieving real-time equipment monitoring and fault prediction through advanced technologies has become a key research area.

[0059] Existing equipment failure prediction methods mainly include the following: one is based on regular inspections by operators, and faults are predicted through the operator's experience and technical level during the inspection process; the other is based on machine learning, which trains machine learning models to learn the normal and failure modes of equipment from large amounts of data, thereby achieving fault prediction.

[0060] While the above solutions can predict equipment failures to a certain extent, they still have some shortcomings. For example, relying on regular operator inspections can be highly subjective and result in large prediction errors. Machine learning models trained using machine learning often rely on a single data source and simple feature extraction methods, and struggle to process large amounts of data in real time, resulting in low prediction accuracy and real-time performance.

[0061] In recent years, the rapid development of the Internet of Things (IoT) has provided new solutions for equipment monitoring and fault prediction. In the embodiments of the present invention, various sensors and communication modules are deployed on target terminal devices, such as generators and transformers, using IoT technology. This enables real-time collection of operating data from the target terminal devices. This data is then processed and analyzed in real time by edge computing devices, thereby enabling real-time monitoring of the target terminal device status and fault prediction.

[0062] The embodiments of the present invention are intended to solve the problems existing in the above-mentioned prior art. Specifically, the concepts of the embodiments of the present invention are centered around the following core points:

[0063] Multi-sensor data acquisition: Vibration data and gas concentration data of target equipment are collected in real time through a sensor group, providing comprehensive equipment status information.

[0064] Real-time data transmission: The collected data is transmitted to the edge computing device in real time through the communication module group to ensure the timeliness of the data.

[0065] Efficient data processing: Perform data preprocessing, feature extraction, and fault prediction on edge computing devices to fully utilize edge computing resources and improve data processing efficiency.

[0066] High-precision fault prediction: By using pre-trained equipment fault prediction models, features are extracted from multimodal data to achieve high-precision fault prediction.

[0067] Fault diagnosis report: Generate a fault diagnosis report based on the fault prediction results to guide maintenance personnel to perform timely maintenance and repairs.

[0068] In summary, the embodiment of the present invention constructs a multi-sensor detection platform based on the Internet of Things, which includes a sensor group, a communication module group, and an edge computing device; the sensor group and the edge computing device are communicatively connected via the communication module group. The sensor group is used to collect first vibration data and first gas concentration data of a target IoT terminal device and upload them to the edge computing device via the communication module group; the edge computing device is used to receive the first vibration data and the first gas concentration data, extract the first vibration feature from the first vibration data, and extract the first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtain a fault prediction result for the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result of the target IoT terminal device. Thus, the first vibration data and the first gas concentration data of the target IoT terminal device are collected in real time through the sensor group and uploaded to the edge computing device, so that the edge computing device uses the device fault prediction model obtained from the cloud server to perform fault prediction based on the first vibration data and the first gas concentration data, so as to timely determine whether the target IoT terminal device has a fault or is about to fail, and generate a fault diagnosis report to guide maintenance personnel to perform timely maintenance and repairs, thereby realizing comprehensive detection of the target IoT terminal device and accurate fault prediction.

[0069] The following briefly describes the structure of the multi-sensor detection platform based on the Internet of Things involved in the embodiments of the present invention. Figure 1 The schematic diagram of the structure of a multi-sensor detection platform based on the Internet of Things provided by the embodiment of the present invention. Figure 1 As shown, the multi-sensor detection platform 100 based on the Internet of Things includes: a sensor group 110, a communication module group 120 and an edge computing device 130; the sensor group 110 and the edge computing device 130 are communicatively connected through the communication module group 120.

[0070] Among them, the sensor group 110 is used to collect the first vibration data and the first gas concentration data of the target IoT terminal device, and upload the first vibration data and the first gas concentration data to the edge computing device 130 through the communication module group 120.

[0071] In an embodiment of the present invention, the target IoT terminal device is a generator and a transformer. Specifically, the sensor group 110 includes a first vibration sensor provided on the generator rotor, for collecting vibration data of the generator rotor. A second vibration sensor provided on the generator stator, for collecting vibration data of the generator stator. A third vibration sensor provided on the transformer core, for collecting vibration data of the transformer core. A fourth vibration sensor provided on the transformer winding, for collecting vibration data on the transformer winding. A first gas sensor provided around the generator, for collecting the SF6 gas concentration around the generator. And a second gas sensor provided in the transformer oil, for collecting the concentration of dissolved gas.

[0072] In order to achieve the purpose of transmitting the collected data to the edge computing device 130 in real time, in an embodiment of the present invention, the communication module group 120 includes a first communication module integrated in the first vibration sensor, a second communication module integrated in the second vibration sensor, a third communication module integrated in the third vibration sensor, a fourth communication module integrated in the fourth vibration sensor, a fifth communication module integrated in the first gas sensor, and a sixth communication module integrated in the second gas sensor.

[0073] The first vibration data is composed of vibration data collected by the first vibration sensor, vibration data collected by the second vibration sensor, vibration data collected by the third vibration sensor, and vibration data collected by the fourth vibration sensor. The first gas concentration data is composed of gas concentration data collected by the first gas sensor and second gas concentration data collected by the second gas sensor.

[0074] The edge computing device 130 is used to receive the first vibration data and the first gas concentration data, extract the first vibration feature from the first vibration data, and extract the first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result of the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result of the target IoT terminal device.

[0075] Specifically, when the edge computing device 130 extracts the first vibration feature from the first vibration data and the first gas concentration feature from the first gas concentration data, and inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result for the target IoT terminal device, a low-pass filter can be used to remove high-frequency noise from the first vibration data to retain low-frequency signal data. The first vibration data from which the high-frequency noise has been removed is smoothed using a moving average method to reduce short-term fluctuations in the data and make the data smoother. In another implementation method, an exponential smoothing method can also be used to smooth the first vibration data from which the high-frequency noise has been removed.

[0076] Next, the smoothed first vibration data is normalized. For example, by the formula The first vibration data after smoothing is normalized to be converted into a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0077] The first vibration data time domain features and the first vibration data frequency domain features are extracted from the normalized first vibration data, where the first vibration data time domain features include mean, variance, peak, kurtosis, and skewness; and the first vibration data frequency domain features include spectrum and power spectrum density.

[0078] When processing the first gas concentration data, edge computing device 130 can use a high-pass filter to remove low-frequency drift from the first gas concentration data to preserve the high-frequency signal. Subsequently, exponential smoothing is used to smooth the first gas concentration data after removing the low-frequency drift, reducing short-term fluctuations in the data and making the data smoother. In another implementation, a moving average method can also be used to smooth the first vibration data after removing high-frequency noise.

[0079] Next, the smoothed first gas concentration data is normalized. For example, by the formula The first vibration data after smoothing is normalized, and its data range is scaled to the range of [0, 1].

[0080] Extract first gas concentration features from the normalized first gas concentration data. The first gas concentration features include the concentration change rate, gas concentration peak value, and gas concentration change trend. Combine the first vibration data time domain features, the first vibration feature frequency domain features, and the first gas concentration features to form a first multimodal feature vector. Input the first multimodal feature vector into a device fault prediction model to obtain a fault prediction result for the target IoT terminal device.

[0081] It should be noted that when the sampling frequencies of the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature are different, the edge computing device 130 can also be used to align the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature to the same time axis to ensure time synchronization of the data. For example, assuming that the sampling frequency of the first vibration data is 1 Hz and the sampling frequency of the first gas concentration data is 1 / 60 Hz (once per minute). We can use linear interpolation to interpolate the gas concentration data to a sampling frequency of 1 Hz.

[0082] When the edge computing device 130 combines the first vibration data time domain features, the first vibration feature frequency domain features and the first gas concentration features into a first multimodal feature vector, it can specifically splice the first vibration data time domain features, the first vibration feature frequency domain features and the first gas concentration features in sequence into the first multimodal feature vector.

[0083] In another implementation, the edge computing device 130 can also use a feature selection model to evaluate the importance of each feature in the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature, and gradually delete the least important features until the number of remaining features is a preset number, and the remaining preset number of features are spliced ​​into a first multimodal feature vector; the first multimodal feature vector includes the most relevant features extracted from the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature.

[0084] In embodiments of the present invention, fault prediction results typically include multiple aspects that can help maintenance personnel and engineers better understand and address potential faults. Exemplary results include: failure probability, predicted time, fault type, fault severity, fault cause, maintenance recommendations, information level, historical data comparison, and alarm information.

[0085] Among them, the failure probability refers to the probability that the target IoT terminal device will fail within a certain period of time in the future as predicted by the device failure prediction model. For example, the probability of a generator failing within the next week is 80%. It is used to help users assess risks and decide whether preventive measures need to be taken.

[0086] The predicted time refers to the specific time or time period that the equipment failure prediction model predicts a failure is likely to occur. For example, a device is expected to fail on November 10, 2024. It can also be a time period, such as a device is expected to fail within the next 30 days. This helps users plan maintenance and ensure necessary inspections and repairs are performed before a failure occurs.

[0087] Fault type refers to the type of fault predicted by the equipment fault prediction model, such as mechanical fault, electrical fault, thermal fault, etc. This helps maintenance personnel prepare the corresponding tools and materials in advance, improving maintenance efficiency.

[0088] Fault severity refers to the severity of a fault predicted by the device fault prediction model, such as minor, moderate, or severe. This can be a categorical label, such as "minor," "moderate," or "severe," or a numerical score, such as 1-5, with 5 being the most severe. This helps users assess the urgency of a fault and decide whether immediate action is necessary.

[0089] Fault causes refer to the causes of failures predicted by the equipment fault prediction model, such as overload, wear, and aging. These are typically text descriptions, such as "overload," "wear," and "aging." This helps maintenance personnel conduct targeted inspections and repairs to prevent similar failures from recurring.

[0090] Maintenance recommendations are suggestions based on fault prediction results, such as replacing parts, cleaning equipment, or adjusting parameters. These recommendations are typically text descriptions, such as "Recommendation for bearing replacement" or "Recommendation for radiator cleaning." These recommendations provide specific maintenance guidance to help maintenance personnel efficiently resolve issues.

[0091] The confidence level refers to the confidence level of the equipment failure prediction model in its prediction results, indicating the reliability of the prediction. It is usually expressed as a percentage. For example, the model's confidence level in the prediction result is 90%. This helps users assess the credibility of the prediction results and decide whether to rely on them for decision-making.

[0092] Historical data comparison compares current prediction results with historical data to analyze trends and changes. This can be done in charts or tables, showing a comparison between historical failure data and current prediction results. This helps identify long-term trends in equipment and assess its health.

[0093] Alarms are triggered when prediction results indicate a potential failure. These can be emails, text messages, or system notifications. These alerts notify relevant personnel immediately, ensuring a swift response.

[0094] The following is a specific example of a failure prediction report:

[0095] Failure prediction report:

[0096] Device number: 12345.

[0097] Forecast date: October 31, 2024.

[0098] Report generation date: October 31, 2024.

[0099] 1. Failure probability:

[0100] -Probability of failure within the next week: 80%

[0101] 2. Prediction time:

[0102] -Expected failure date: November 10, 2024

[0103] 3. Fault type:

[0104] -Predicted failure type: mechanical failure

[0105] 4. Fault severity:

[0106] -Predicted failure severity: Medium (3 / 5)

[0107] 5. Cause of failure:

[0108] -Predicted failure cause: bearing wear

[0109] 6. Maintenance suggestions:

[0110] -Recommended actions:

[0111] -Replace worn bearings.

[0112] - Check the lubrication system to ensure that there is sufficient lubricating oil.

[0113] - Clean the radiator to ensure good heat dissipation.

[0114] 7. Confidence Level:

[0115] - Forecast confidence level: 90%

[0116] 8. Historical data comparison:

[0117] -Historical failure data:

[0118] - January 10, 2024: Mechanical failure (bearing wear).

[0119] - March 20, 2024: Electrical failure (aging of circuit boards).

[0120] -Current prediction results:

[0121] - November 10, 2024: Mechanical failure (bearing wear).

[0122] 9. Alarm information:

[0123] -Alarm level: High

[0124] -Alarm content: Equipment 12345 has an 80% probability of mechanical failure in the next week. Please arrange maintenance as soon as possible.

[0125] - Notification method: Email, SMS.

[0126] In an embodiment of the present invention, a sensor group 110, a communication module group 120, and an edge computing device 130 are provided; the sensor group 110 and the edge computing device 130 are communicatively connected via the communication module group 120. The sensor group 110 is used to collect first vibration data and first gas concentration data of a target IoT terminal device and upload the data to the edge computing device 130 via the communication module group 120; the edge computing device 130 is used to receive the first vibration data and the first gas concentration data, extract a first vibration feature from the first vibration data, and extract a first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtain a fault prediction result for the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result for the target IoT terminal device. Thus, the first vibration data and the first gas concentration data of the target IoT terminal device are collected in real time through the sensor group 110 and uploaded to the edge computing device 130, so that the edge computing device 130 uses the device fault prediction model obtained from the cloud server 210 to perform fault prediction based on the first vibration data and the first gas concentration data, so as to timely determine whether the target IoT terminal device has a fault or is about to fail, and generate a fault diagnosis report to guide maintenance personnel to perform timely maintenance and repairs, thereby achieving comprehensive detection of the target IoT terminal device and accurate fault prediction.

[0127] Figure 2 This is a schematic diagram of another multi-sensor detection platform based on the Internet of Things provided by an embodiment of the present invention. Figure 2 As shown, based on the above embodiments, the multi-sensor detection platform based on the Internet of Things of this embodiment may further include: a cloud server 210; the cloud server 210 is used to collect historical vibration data and historical gas concentration data of the target Internet of Things terminal device from the edge computing device 130, the historical vibration data and historical gas concentration data include data of the target Internet of Things terminal device in a normal state and data in a fault state, and use the collected historical vibration data and historical gas concentration data to train the equipment fault prediction model to be trained, and send the trained equipment fault prediction model to the edge computing device 130.

[0128] Specifically, when training a device fault prediction model, cloud server 210 first collects historical vibration data and gas concentration data from the target IoT terminal device. It then determines whether the sample data contains faults within a preset time window, generating labels for the historical vibration data and gas concentration data. Each preset time window corresponds to a sample data item, and each sample data item includes historical vibration data and historical gas concentration data.

[0129] Based on the data within a time window, determining whether a fault exists within that window can be done by labeling it in the following ways: Classification labeling: Labeling a window as "normal" or "faulty" if the data within the window corresponds to a fault event. Multi-category labeling: If there are multiple fault types, the window can be labeled with different fault categories. Regression labeling: If you need to predict the severity of a fault, you can use a numerical label (for example, fault level).

[0130] Next, the labeled sample data is integrated into the training dataset. Each sample in the training dataset contains historical vibration data, historical gas concentration data, and a corresponding label. Optionally, the edge computing device 130 also checks the ratio of normal to faulty samples in the training dataset and, if necessary, performs upsampling or downsampling to balance the dataset and prevent the model from favoring a particular label. The training dataset is divided into a training set and a test set. The training set is used to train the basic fault prediction model, and the test set is used to test the performance of the trained basic fault prediction model.

[0131] Among them, the basic fault prediction model can be an integrated learning model composed of a random forest classifier and a support vector machine classifier. It combines the advantages of the random forest classifier in processing high-dimensional data and nonlinear relationships, and the support vector machine classifier in processing small samples and high-dimensional data, and has strong generalization capabilities, forming an integrated learning model with stronger performance.

[0132] In one implementation, the random forest classifier and support vector machine classifier of the basic fault prediction model can be implemented based on a voting mechanism. The constructed basic fault prediction model is trained using sample data from the training set to obtain a trained basic fault prediction model. The trained basic fault prediction model is used to predict data from the test set, and performance indicators of the trained basic fault prediction model are calculated based on the prediction results. A receiver operating characteristic (ROC) curve is generated based on the calculated performance indicators to evaluate the performance of the trained basic fault prediction model. When the performance evaluation of the trained basic fault prediction model passes, a device fault prediction model is obtained.

[0133] In another implementation, the random forest classifier and support vector machine classifier of the basic fault prediction model can be implemented based on a stacking mechanism. The basic fault prediction model constructed based on the stacking mechanism is trained using sample data in the training set, and the prediction results of the basic fault prediction model are generated using cross-validation. The prediction results of the basic fault prediction model are used as new features to train the meta-model (meta-model 1). After that, the basic fault prediction model is used to predict the data in the test set to obtain the corresponding prediction results. The prediction results obtained this time are used as new features to predict using the trained meta-model to generate the final prediction results. Based on the final prediction results, the performance indicators of the trained basic fault prediction model are calculated; an ROC curve is generated based on the calculated performance indicators to evaluate the performance of the trained basic fault prediction model; when the performance evaluation of the trained basic fault prediction model is qualified, the equipment fault prediction model is obtained.

[0134] It is understood that, in addition to training the device fault prediction model, the cloud server 210 may also store the first vibration data and the first gas concentration data of the target IoT terminal, as well as the corresponding target IoT terminal operating status, fault prediction report, and other relevant information. Detailed description is omitted here.

[0135] According to the detection platform of an embodiment of the present invention, the device fault prediction model can be pre-trained through a cloud server, and the trained device fault prediction model can be sent to the edge computing device, so that data processing, feature extraction and fault prediction can be performed on the edge computing device, making full use of the computing resources of edge computing and improving data processing efficiency.

[0136] The multi-sensor detection platform based on the Internet of Things provided by the embodiment of the present invention is described above. The detection method applied in the multi-sensor detection platform based on the Internet of Things is described in detail below with reference to the accompanying drawings.

[0137] Figure 3 FIG. 1 is a flow chart showing a multi-sensor detection method based on the Internet of Things provided by an embodiment of the present invention. Figure 3 As shown, the method includes the following steps 301 to 304:

[0138] Step 301: The sensor group collects first vibration data and first gas concentration data of the target IoT terminal device.

[0139] Step 302: The sensor group uploads the first vibration data and the first gas concentration data to the edge computing device through the communication module group.

[0140] Step 303: The edge computing device receives the first vibration data and the first gas concentration data, extracts the first vibration feature from the first vibration data, and extracts the first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device.

[0141] Step 304: The edge computing device outputs a fault diagnosis report based on the fault prediction result of the target IoT terminal device.

[0142] like Figure 4 As shown, in the above step 303, the edge computing device extracts the first vibration feature from the first vibration data and the first gas concentration feature from the first gas concentration data, and inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result for the target IoT terminal device. Specifically, the process may include the following steps 401 to 406:

[0143] Step 401: The edge computing device uses a low-pass filter to remove high-frequency noise in the first vibration data, uses a moving average method to smooth the first vibration data from which the high-frequency noise has been removed, and normalizes the smoothed first vibration data.

[0144] Step 402: The edge computing device extracts time domain features of the first vibration data and frequency domain features of the first vibration data from the normalized first vibration data. The time domain features of the first vibration data include mean, variance, peak, kurtosis, and skewness; the frequency domain features of the first vibration data include spectrum and power spectral density.

[0145] Step 403: The edge computing device uses a high-pass filter to remove low-frequency drift in the first gas concentration data, uses an exponential smoothing method to smooth the first gas concentration data from which the low-frequency drift is removed, and normalizes the smoothed first gas concentration data.

[0146] Step 404: The edge computing device extracts the first gas concentration characteristics from the normalized first gas concentration data. The first gas concentration characteristics include the concentration change rate, the gas concentration peak value, and the gas concentration change trend.

[0147] Step 405: The edge computing device combines the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature into a first multimodal feature vector.

[0148] It can be understood that when the sampling frequencies of the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature are different, the edge computing device aligns the first vibration data time domain feature, the first vibration feature frequency domain feature, and the first gas concentration feature onto the same time axis to ensure time synchronization of the data. For example, assuming that the sampling frequency of the first vibration data is 1 Hz and the sampling frequency of the first gas concentration data is 1 / 60 Hz (once per minute), we can use linear interpolation to interpolate the gas concentration data to a sampling frequency of 1 Hz.

[0149] Step 406: The edge computing device inputs the first multimodal feature vector into the device fault prediction model to obtain a fault prediction result for the target IoT terminal device.

[0150] The multi-sensor detection method based on the Internet of Things (IoT) of the present invention can be applied to a multi-sensor detection platform based on the IoT. The method collects first vibration data and first gas concentration data of a target IoT terminal device through a sensor group; uploads the first vibration data and first gas concentration data to an edge computing device through a communication module group; receives the first vibration data and first gas concentration data through the edge computing device, extracts a first vibration feature from the first vibration data, and extracts a first gas concentration feature from the first gas concentration data; inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result for the target IoT terminal device; and outputs a fault diagnosis report based on the fault prediction result for the target IoT terminal device through the edge computing device. Thus, the first vibration data and first gas concentration data of the target IoT terminal device are collected in real time by the sensor group and uploaded to the edge computing device. The edge computing device then uses the device fault prediction model obtained from the cloud server to perform a fault prediction based on the first vibration data and the first gas concentration data, thereby promptly determining whether the target IoT terminal device is faulty or about to fail, and generating a fault diagnosis report to guide maintenance personnel in performing timely maintenance and repairs, thereby achieving comprehensive detection and accurate fault prediction for the target IoT terminal device.

[0151] Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present invention. Figure 5 As shown, this embodiment provides an electronic device 500 including: a processor 501 and a memory 502; wherein:

[0152] The memory 502 is used to store computer programs, and the memory may also be a flash memory.

[0153] The processor 501 is configured to execute the execution instructions stored in the memory to implement the steps corresponding to the various execution entities in the above method.

[0154] For example, when the electronic device 500 is a sensor group, the memory 502 is used to store computer programs, and the processor 501 is used to execute the execution instructions stored in the memory to collect the first vibration data and the first gas concentration data of the target IoT terminal device.

[0155] For example, when the electronic device 500 is a communication module group, the memory 502 is used to store computer programs, and the processor 501 is used to execute the execution instructions stored in the memory to upload the first vibration data and the first gas concentration data to the edge computing device.

[0156] For another example, when the electronic device 500 is an edge computing device, the memory 502 is used to store computer programs, and the processor 501 is used to execute execution instructions stored in the memory, receive first vibration data and first gas concentration data, extract first vibration features from the first vibration data, and extract first gas concentration features from the first gas concentration data, input the first vibration features and the first gas concentration features into a pre-trained device fault prediction model to obtain a fault prediction result of the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result of the target IoT terminal device.

[0157] Optionally, the memory 502 may be independent or integrated with the processor 501 .

[0158] When the memory 502 is a device independent of the processor 501, the electronic device 500 may further include:

[0159] The bus 503 is used to connect the memory 502 and the processor 501 .

[0160] This embodiment further provides a computer-readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the various aforementioned embodiments.

[0161] This embodiment further provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor can execute the computer program to cause the electronic device to implement the methods provided in the various embodiments described above.

[0162] Those skilled in the art will readily recognize other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present embodiments are intended to cover any variations, uses, or adaptations of the present embodiments that follow the general principles of the present embodiments and include common knowledge or customary techniques in the art not disclosed in the present embodiments. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present embodiments being indicated by the claims.

[0163] It should be understood that the embodiments of the present invention are not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the embodiments of the present invention is limited only by the appended claims.

Claims

1. A multi-sensor detection platform based on the Internet of Things, characterized by: It includes a sensor group, a communication module group and an edge computing device; the sensor group and the edge computing device are communicatively connected through the communication module group; The sensor group is used to collect first vibration data and first gas concentration data of a target IoT terminal device, and upload the first vibration data and the first gas concentration data to the edge computing device through the communication module group; The edge computing device is configured to receive the first vibration data and the first gas concentration data, extract a first vibration feature from the first vibration data, and extract a first gas concentration feature from the first gas concentration data, input the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model to obtain a fault prediction result for the target IoT terminal device; and output a fault diagnosis report based on the fault prediction result for the target IoT terminal device; The edge computing device extracts a first vibration feature from the first vibration data and a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device, specifically for: removing high-frequency noise from the first vibration data using a low-pass filter, smoothing the first vibration data from which the high-frequency noise has been removed using a moving average method, and normalizing the smoothed first vibration data; extracting the first vibration features from the normalized first vibration data, wherein the first vibration features include time domain features of the first vibration data and frequency domain features of the first vibration data, wherein the time domain features of the first vibration data include mean, variance, peak, kurtosis, and skewness; and the frequency domain features of the first vibration data include frequency spectrum and power spectral density; using a high-pass filter to remove low-frequency drift in the first gas concentration data, smoothing the first gas concentration data after removing the low-frequency drift using an exponential smoothing method, and normalizing the smoothed first gas concentration data; extracting the first gas concentration features from the normalized first gas concentration data, where the first gas concentration features include a concentration change rate, a gas concentration peak value, and a gas concentration change trend; combining the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature into a first multimodal feature vector; The first multimodal feature vector is input into the device fault prediction model to obtain a fault prediction result of the target IoT terminal device.

2. The platform according to claim 1, characterized in that When the sampling frequencies of the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature are different, the edge computing device is further configured to: The first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature are aligned on the same time axis.

3. The platform according to claim 1, characterized in that When the edge computing device combines the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature into a first multimodal feature vector, the edge computing device is specifically configured to: Sequentially concatenating the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature into the first multimodal feature vector; or, Using a feature selection model, the importance of each feature among the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature is evaluated, and the least important features are gradually deleted until the number of remaining features reaches a preset number, and the remaining preset number of features are spliced ​​into the first multimodal feature vector; the first multimodal feature vector includes the most relevant features extracted from the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature.

4. The platform according to claim 1, characterized in that The platform also includes: a cloud server; The cloud server is used to collect historical vibration data and historical gas concentration data of the target IoT terminal device from the edge computing device, wherein the historical vibration data and the historical gas concentration data include data of the target IoT terminal device in a normal state and data in a faulty state, and use the collected historical vibration data and historical gas concentration data to train the equipment fault prediction model to be trained, and send the trained equipment fault prediction model to the edge computing device.

5. The platform according to claim 4, characterized in that When training the equipment failure prediction model to be trained, the cloud server is specifically used to: Determining whether the sample data has a fault according to a preset time window to generate labels for the historical vibration data and the historical gas concentration data; wherein each preset time window corresponds to one sample data, and each sample data includes the historical vibration data and the historical gas concentration data; Integrating the labeled sample data into a training data set, wherein each sample data in the training data set includes the historical vibration data, the historical gas concentration data, and a corresponding label; Dividing the training data set into a training set and a test set; Using the sample data in the training set to train the constructed basic fault prediction model to obtain the trained basic fault prediction model; Using the trained basic fault prediction model to predict the data in the test set, and calculating the performance index of the trained basic fault prediction model according to the prediction results; Generating an ROC curve based on the calculated performance index to evaluate the performance of the trained basic fault prediction model; When the performance evaluation of the trained basic fault prediction model is qualified, the equipment fault prediction model is obtained.

6. The platform according to claim 1, characterized in that The target IoT terminal device includes a generator and a transformer; The sensor group includes a first vibration sensor provided on the generator rotor, a second vibration sensor provided on the generator stator, a third vibration sensor provided on the transformer core, a fourth vibration sensor provided on the transformer winding, a first gas sensor provided around the generator, and a second gas sensor provided in the transformer oil; The communication module group includes a first communication module integrated in the first vibration sensor, a second communication module integrated in the second vibration sensor, a third communication module integrated in the third vibration sensor, a fourth communication module integrated in the fourth vibration sensor, a fifth communication module integrated in the first gas sensor, and a sixth communication module integrated in the second gas sensor.

7. A multi-sensor detection method based on the Internet of Things, applied to the multi-sensor detection platform based on the Internet of Things as claimed in any one of claims 1 to 6, characterized in that: The method comprises: The sensor group collects first vibration data and first gas concentration data of the target IoT terminal device; The sensor group uploads the first vibration data and the first gas concentration data to the edge computing device through the communication module group; The edge computing device receives the first vibration data and the first gas concentration data, extracts a first vibration feature from the first vibration data, extracts a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device; The edge computing device outputs a fault diagnosis report according to the fault prediction result of the target IoT terminal device; The edge computing device extracts a first vibration feature from the first vibration data and a first gas concentration feature from the first gas concentration data, inputs the first vibration feature and the first gas concentration feature into a pre-trained device fault prediction model, and obtains a fault prediction result for the target IoT terminal device, including: The edge computing device uses a low-pass filter to remove high-frequency noise from the first vibration data, uses a moving average method to smooth the first vibration data after the high-frequency noise is removed, and normalizes the smoothed first vibration data; The edge computing device extracts the first vibration feature from the normalized first vibration data, where the first vibration feature includes a time domain feature of the first vibration data and a frequency domain feature of the first vibration data. The time domain feature of the first vibration data includes a mean, a variance, a peak value, a kurtosis, and a skewness. The frequency domain feature of the first vibration data includes a frequency spectrum and a power spectrum density. The edge computing device uses a high-pass filter to remove low-frequency drift in the first gas concentration data, uses an exponential smoothing method to smooth the first gas concentration data after the low-frequency drift is removed, and normalizes the smoothed first gas concentration data; The edge computing device extracts the first gas concentration feature from the normalized first gas concentration data, where the first gas concentration feature includes a concentration change rate, a gas concentration peak value, and a gas concentration change trend; The edge computing device combines the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature into a first multimodal feature vector; The edge computing device inputs the first multimodal feature vector into the device fault prediction model to obtain a fault prediction result of the target IoT terminal device.

8. The method according to claim 7, characterized in that The method further comprises: When the sampling frequencies of the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature are different, the edge computing device aligns the first vibration data time domain feature, the first vibration data frequency domain feature, and the first gas concentration feature on the same time axis.

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