Multi-sensor Information Fusion Technology Research Articles

Identification of Freshwater Fish Meat Freshness Based on Multi-Sensor Fusion Technology

In order to explore the application of multi-sensor fusion technology in nondestructive testing of agricultural products and develop new detection methods for agriculture, multi-sensor information fusion technology are performed to identify freshness of freshwater fish meat. Different freshness of grass carp specimens were identified by multi-sensor fusion method which three characteristic data that are PH value, conductance and smell were measured, collected, and then fused by fuzzy theory method. During the experiment process the value of the total volatile basic nitrogen (TVB-N) of standard samples and test samples were measured. The freshness standard was established by the TVB-N value of standard samples. Correctness of the results of multi-sensor data fusion was verified by comparing the TVB-N value of the test samples with the freshness standard. The results show that the freshwater fish meat with different freshness can be identified correctly by multi-sensor data fusion method which is fuzzy theory and the accuracy rate is 94%.

The Investigation of the Obstacle Avoidance for Mobile Robot Based on the Multi Sensor Information Fusion technology

The Investigation of the Obstacle Avoidance for Mobile Robot Based on the Multi Sensor Information Fusion technology

Application of multi-sensor information fusion technology on fault diagnosis of hydraulic system

The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in fault diagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic fault diagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for fault diagnosis of hydraulic system.

The Application of Distributed Multi-Sensor Information Fusion Technology in Underwater Vehicle

The application of distributed multi-sensor information fusion technology in accurate positioning of Underwater Vehicle was introduced in this paper. According to the system structure of Distributed multi-sensor in an AUV “T1”, this article establishes the Kalman filtering mathematical model, accomplishes the fusion algorithm based on Kalman filtering and a numerical simulation. The experimental result shows that the application of fusion algorithm based on Kalman filtering can avoid the limitations of a single sensor, reduce its uncertainty impact and increase the confidence level of data.

Information Fusion Technology and its Application to Intelligent Control System of Ceramic Kiln

This paper applies multi-sensor information fusion technology to intelligent control system of ceramic kiln and proposes a corresponding control scheme. The control scheme is composed of six-layer structures which include four subsystems. Then, compositions, functions and adoptive algorithms of the four subsystems are analyzed. In the end, the whole system control scheme and its work process are analyzed.

Application of Multi-Sensor Information Fusion Technology in Machine-Tool Monitoring

The structure and applications of multi-sensor information fusion technology are introduced systematically in this paper. Main fusion algorithms are summarized. The fusion applications are pointed out in machine-tool monitoring. The structure and information process of monitor system are discussed in the machine-tool monitoring.

Study on Oil Quality Evaluation System and its Implementation Based on Multi-Information Fusion

This paper presents the oil quality evaluation system and establishes the two-stage fusion model based on multi-sensor information fusion technology. It also develops the oil quality evaluation model based on neural network model. With the advantages of multi-source information technology, the model implements comprehensive evaluation for oil quality, and provides a set of neural network training process and its results which achieve the oil quality evaluation based on information fusion. The case study shows that the prediction results for four kinds of oil samples by evaluation model based on multi-source fusion are consistent with the actual results. The comparison between operation test trend predictions and actual tests also shows the correctness of the oil quality evaluation model. The proposed multi-information fusion technology for oil quality evaluation system improves the evaluation accuracy and reduces dependence on technical personnel’s analysis experience, which is of great importance for improving the technical management level and the awareness of oil lubrication properties.

Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion

From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

The Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion

From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

Application on Automatic Chemical Detection Technique Based on NN

Take example for the Neural Networks Distinguishing Chemical Agents (NNDCA) model, the study and application about the multi-sensors information fusion (MSIF) technology is researched, the model of Naval Ships Chemical Detection (NSCD) system based on multi-sensors fusion is built, the basic method of the NNDCA is analyzed, and the realization idea of the NNDCA model is put forward. For fastness and accuracy and automation, connecting the wavelet analysis with the neural networks organically, the model of the NNDCA by the returning neural networks with deviation unit and the method of the feature extraction for the chemical agents based on the wavelet analysis are established. After analyzing and discussing the results a conclusion is drawn that it is a kind of valid method to realize the automation and intelligence of distinguishing chemical agents using the neural networks combined with the multi-sensors.

Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion

This paper used multi-sensor information fusion technology in pulsed gas tungsten arc welding. Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, voltage, weld pool image, and weld sound information about the pulsed gas tungsten arc welding process, and special algorithms were designed to extract the respective signal features of different sensors’ information. Then D-S evidence theory was used to fuse the different signal features to predict the penetration status about the welding process. Aimed at the difficulty of obtaining basic probability assignment in D-S evidence theory, back-propagation (BP) neural network was used to obtain the basic probability assignment. Experiments were done to obtain data for training the BP neural network and test the prediction reliability of D-S evidence theory information fusion, and comparison results showed that D-S evidence theory could effectively use the information obtained by different sensors and obtain better prediction result than single sensor.