Bolted joint is widely used in mechanical and architectural structures, such as machine tools, industrial robots, transport machines, power plants, aviation stiffened plate, bridges, and steel towers. The bolt loosening induced by flight load and environment factor can cause joint failure leading to a disastrous accident. Hence, structural health monitoring is critical for the bolted joint detection. In order to realize a real-time and convenient monitoring and satisfy the requirement of advanced maintenance of the structure, this paper proposes an intelligent bolted joint failure monitoring approach using a developed decision fusion system integrated with Lamb wave propagation based actuator-sensor monitoring method. Firstly, the basic knowledge of decision fusion and classifier selection techniques is briefly introduced. Then, a developed decision fusion system is presented. Finally, three fusion algorithms, which consist of majority voting, Bayesian belief, and multiagent method, are adopted for comparison in a real-world monitoring experiment for the large aviation aluminum plate. Based on the results shown in the experiment, a big potential in real-time application is presented that the method can accurately and rapidly identify the bolt loosening by analyzing the acquired strain signal using proposed decision fusion system.
Bolted joint is widely used in mechanical and architectural structures, such as machine tools, industrial robots, transport machines, power plants, aviation stiffened plate, bridges, and steel towers. The bolt loosening induced by flight load and environment factor can cause joint failure leading to a disastrous accident for the aircraft. In order to keep up the integrity and operation safety of these structures, detecting bolted joint in real time is an important concern in structural health monitoring.
Till now, for the bolt loosening detection, there are some conventional nondestructive inspection techniques, which use the ultrasonic waves and electromagnetic resonance [
The problem of detecting bolt loosening has been studied by different researchers. The principle in these techniques is to seek out the changes in the dynamic properties as indicators of damage in the structure. Pai and Hess study the loosening of threaded fasteners due to shear loads, as well as the effect of fastener placement on a structure as a variable promoting self-loosening [
Recently, the development of artificial intelligence techniques has led to their application in the structure health monitoring. Some methods, such as artificial neural networks and support vector machines, have been employed to estimate the structure damage [
This paper proposes an intelligent bolted joint failure monitoring approach using a developed decision fusion system integrated with Lamb wave propagation-based actuator-sensor monitoring method. Firstly, the basic knowledge of decision fusion and classifier selection techniques is briefly introduced. Then, a developed decision fusion system is presented. Finally, three fusion algorithms, which consist of majority voting, Bayesian belief, and multiagent method, are adopted for comparison in a real-world monitoring experiment for the large aviation aluminum plate. Based on the results shown in the experiment, a big potential in real-time application is presented that the method can accurately and rapidly identify the bolt loosening by analyzing the acquired strain signal using the proposed decision fusion system.
The rest of this paper is structured in the following manner. Section
This section covers a brief introduction of decision fusion method, which consists of the classifier selection based on entropy and multiagent fusion algorithm.
Studies [
Generally, the diversity of classifiers can give more effective information, so smaller correlation degree among the classifiers can lead to better fusion performance. According to the diversity measurement principle, it is neccesacy to select a team of classifiers and the flowchart of classifier selection can be shown in Procedure
Define: Begin:
Note, when a similar low correlation degree appears for more than one classifier, the classifier that has the highest accuracy rate is chosen.
Generally, the classifiers’ output information can be divided into three levels [ the abstract level: a classifier the rank level: a classifier the measurement level: a classifier
Among the levels mentioned above, from the abstract level to the measurement level, the amount of information of the classifiers’ output increases in sequence. Accordingly, the classification algorithms of the measurement information can produce the best results. However, the classifiers that can supply the abstract information are more available in the real application.
According to the three levels in the classifiers’ output information, decision fusion methods can be divided into three types. Multiple classifiers’ fusion integrates different decisions from multiple classifiers to boost the accuracy of recognition. The decision fusion methods of the used abstract information are widely adopted, which include majority voting [
In the section, the multiagent fusion algorithm is introduced in detail. In recent years, multiagent system (MAS) of artificial intelligence (AI) has been a natural model for developing a large-scale, complex, distributed system, which is loosely coupled and heterogeneous [
In the multiagent fusion method, each classifier is deemed as a single agent. The confusion matrix of the classifier denotes the recognition ability of the agent. For a test sample, Bayesian belief decision can be given by each classifier agent. A two-order correlation degree for information exchange between any two classifiers is introduced to dynamically modify each agent’s belief decision. Once there are no more different decisions for these agents, a final combination decision is made. Hence, Bayesian belief method and majority voting are integrated creatively in the method. It considers a behaviour of population decision. The flowchart of multiagent method is shown in Figure
Flowchart of multiagent decision fusion algorithm.
Firstly, a sample set
Confusion matrix
Secondly, a five-dimensional codecision matrix
The element
After obtaining the confusion matrix and codecision matrix, the initial belief matrix
Next, if the initial maximum vote rate is less than an accordance threshold, there are more different decisions for the classifier agents. Then, the agents can interact with each other and modify the original belief degrees themselves using the codecision matrix. The repeated modification scheme is represented as
Whenever the belief matrix is modified, a normalization process is required to ensure the row element of new belief matrix being the significant probability value. On the basis of the new belief matrix, a decision vector of the classifier agents is acquired to generate a new vote rates. If the maximum vote rate is still less than the predetermined threshold, the classifier agents have less accordance for the input sample. Hence, the interaction between the agents will continue and their belief matrix will be modified repeatedly until their decision reaches the accordance criterion. Finally, the multiagent classifiers use a majority voting method to give out the output of fusion decision.
The active SHM method is generally adopted to monitor the joint failure induced by bolt loosening [
The active monitoring method for bolt loosening.
Single-actuator multisensor
Cycle-actuator multisensor
Generally, a sine wave can be excited by the PZT actuator to the structure at a frequency, under which the vibration response of the structure is sensitive to the bolt loosening. The experiment shows that the sensor signal varies before and after the bolt loosening [
Sensor layout and joint failure position on the specimen.
In this paper, a decision fusion system is presented for bolted joint monitoring. It is based on a self-designed fusion diagnosis toolbox by MATLAB language R2006a. This system consists of six levels: sensor, feature extraction, feature combination, multiclassifier decision, classifier selection, and decision fusion. The framework of the proposed system is shown in Figure
Framework of the proposed fusion decision system.
In order to verify the effectiveness of the presented decision fusion system integrated with Lamb wave propagation based actuator-sensor monitoring method, in this paper, the large aviation aluminum plate structure is studied as the experimental object. Figure
System setup.
In this study, tests are conducted with healthy and unhealthy configuration which includes the full loose state of 20 bolts in different locations around the structure. Hence, twenty joint failure patterns and one health pattern are considered. In the experiment, tests are conducted with healthy and damage configuration which includes the completely loose state of 20 bolts in different locations around the structure, and each time only one bolt is loosening. In order to quantitatively measure the loosening degrees of bolt, the tightening condition
Hence, the various cases tested are (i) healthy case: the structure is tested without any bolt loosening from the joint. (ii) unhealthy: the cases tested are the complete loosening of Bolts 2, 5, 9, 19, 22, 23, 24, 25, 26, 27, 33, 39, 43, 49, 50, 51, 52, 53, 54, or 63 as shown in Figure
The loosening bolts monitored location.
In the experiment, twelve PZT sensors around the boundary are employed to detect the bolt loosening with the cycle-actuator multisensor method. For the measurement hardware, the self-design integrate and program control multichannel piezoelectric scanning system is used in the active monitoring for bolt loosening [
The principle structure of the active monitoring system.
The computer controls twelve PZT sensors circularly and periodically to excite and sense the structure strain signal. The excitation signal is the sine wave with 100 KHz. Lots of experiments [
The sensor signal change before and after Bolt 9 loosening.
The excited sine signal
The signal before bolt loosening
The sensor signal after bolt loosening
Six pattern classification methods are utilized to identify the loosening bolt. The utilized classifiers are described as follows. Support vector machine (SVM): the method can implement the good recognition rate derived from a few training samples, and it is based on statistical learning theory [ C4.5: the algorithm implements “If-Then” rules derived from the training data set [ Improved iterative scaling (IIS): IIS is one of the major algorithms for finding the optimal parameters for the conditional exponential model [ Gaussian mixture model (GMM): the classifier is based on Gaussian component functions [ Learning vector quantization (LVQ): it is a neural network classifier proposed by Villmann et al. [
This section describes the result of an experiment of the bolted joint monitoring using the proposed decision fusion system. Then, comparison and discussion are given for each part of the presented system.
Next, six classifiers are utilized to classify the calculated features of the bolt loosening. The relevant parameters setup for these classifiers can be found in Table
Parameters of individual classifier.
Classifier | SVM | C4.5 |
|
IIS | LVQ |
---|---|---|---|---|---|
Parameters setup | Kernel function: |
Percentage of incorrectly assigned samples at a node = 5 |
|
Number of iterations = 50 | Number of neurons = 50, epochs = 50 |
Classification results.
Classifier | SVM | C4.5 |
|
IIS | GMM | LVQ |
|
||||||
Accuracy | 0.8952 | 0.5385 | 0.8571 | 0.1904 | 0.7524 | 0.3077 |
Based on the individual classification decisions acquired in the first step, the entropy-based diversity measure method introduced in Section
Result of optimal sequence of classifiers fused.
Number of classifiers selected | Serial number of classifiers | Entropy-based diversity measure | |||||
---|---|---|---|---|---|---|---|
1 | 1 | — | |||||
2 | 1 | 4 | 1.127 | ||||
3 | 1 | 4 | 6 | 0.978 | |||
4 | 1 | 4 | 6 | 2 | 0.878 | ||
5 | 1 | 4 | 6 | 2 | 3 | 0.826 | |
6 | 1 | 4 | 6 | 2 | 3 | 5 | 0.596 |
To evaluate the effect of classifier selection, Bayesian fusion method with classifier selection is compared with the one without classifier selection as shown in Figure
Effect of classifiers selection (Bayesian method).
After the six classifiers are sequentially selected, the decision vectors of multiclassifiers are fused using three fusion methods, namely, majority voting, Bayesian belief, and multiagent method. In the multiagent method, accordance criterion is a vital parameter. The larger the value is configured, the longer computation time it takes and the better accuracy rate it produces. In order to search the optimization value, the value is traversed from 0.5 to 1 with a step size of 0.05 and the corresponding fusion results are shown in Table
Relationship of accordance criterion, number of classifiers fused, and accuracy.
Accordance criterion |
Number of classifiers fused | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
Accuracy | ||||||
0.50 | 0.895 | 0.933 | 0.962 | 0.952 | 0.949 | 0.956 |
0.55 | 0.895 | 0.933 | 0.962 | 0.952 | 0.949 | 0.956 |
0.60 | 0.895 | 0.933 | 0.962 | 0.952 | 0.949 | 0.956 |
0.65 | 0.895 | 0.933 | 0.962 | 0.952 | 0.949 | 0.956 |
0.70 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
0.75 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
0.80 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
0.85 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
0.90 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
0.95 | 0.895 | 0.933 | 0.962 | 0.952 | 0.971 | 0.971 |
The performance of the three fusion algorithms is compared as shown in Figure
Fusion performances of three algorithms for current data.
In this paper, a decision system for bolted joint monitoring is presented which consists of individual classification, classifier selection, and decision fusion. The effectiveness of the proposed methodology is tested with examples of the large aviation aluminum plate structure. In the process, classification accuracy considering the classifier selection is superior to the ones without the step. To compare three fusion methods, the multiagent method is the best since the method not only considers the character of individual classifiers, but also the information exchange between the classifiers. Decision fusion strategy can improve the classification accuracy remarkable.
Based on the decision fusion framework, further studies are required concentrating on the following three parts: investigating more joint failure modes including the level of the bolt loosening and validating the effectiveness of the presented method; more studies are needed with complex structures to fully validate the new method; comparing other different methods of classifier selection and evaluating these methods; studying deeply the relation among the individual classifier, classifier selection, and fusion method.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is supported by the National Natural Science Foundation of China (Grant no. 51405409) and the Fundamental Research Funds for the Central Universities.