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Belt conveyor is widely used for material transportation over both short and long distances nowadays while the failure of a single component may cause fateful consequences. Accordingly, the use of machine learning in timely fault diagnosis is an efficient way to ensure the safe operation of belt conveyors. The support vector machine is a powerful supervised machine learning algorithm for classification in fault diagnosis. Before the classification, the principal component analysis is used for data reduction according to the varieties of features. To optimize the parameters of the support vector machine, this paper presents a grey wolf optimizer approach. The diagnostic model is applied to an underground mine belt conveyor transportation system fault diagnosis on the basis of monitoring data collected by sensors of mine internet of things. The results show that the recognition accuracy of the fault is up to 97.22% according to the mine site dataset. It is proved that the combined classification model has a better performance in fault intelligent diagnosis.

As the widely used mechanical transportation equipment, the belt conveyor plays a critical role in transporting raw coal in the coal mining industry and other industries. In recent years, with the widespread automation in mines, accidents occurring involving mining equipment have become more and more complex. In some developing countries such as China, there have been frequent incidents of miners’ casualties and property loss caused by the failure of mine equipment like belt conveyer. Generally, the accidents are mainly caused by improper use or maintenance of the belt conveyor or not timely detection of the system fault, which compounds difficulties to diagnose the equipment failure in real time when the fault occurs. Meanwhile, the development of the internet of things has brought out plenty of big data which indicate system error information in advance. Besides, timely fault detection could be a great help for belt conveyor predictive maintenance [

At present, researches on fault diagnosis are basically divided into three modules, namely, (i) fault mechanisms; (ii) monitoring signal acquisition and processing; and (iii) intelligent fault diagnosis. In the fields of fault mechanisms, Miriam and Anna [

Although there have been research works on the fault diagnosis of mechanical equipment, diagnostic techniques for the overall failure of the belt conveyor has received little attention. Belt conveyor equipment is a kind of essential equipment in coal mine transportation, where safety and stability directly affect continuous production. In coal mines, it is significant to carry out effective fault diagnosis of the belt conveyor [

The rest of the content is organized as follows.

Section

In this section, the principal component analysis (PCA) is introduced for data dimension reduction.

Suppose

A single element of the

According to formula (

To ensure the orthogonality of the principal component vectors,

In equation (

In order to ensure that all of the above formulas are true, the sample data should meet the following constraints, and the

Then, it obtains

The above equation is the characteristic equation in the line generation.

The feature value is obtained by using equation (

In the actual sample data processing, the principal component contribution rate is introduced to represent the amount of information that the data sample reflects in the original sample data. The larger the proportion of the principal component contribution rate, the more representative the data sample itself and the contribution to any principal component vector. The rate is expressed as

For multidimensional sample data, principal component analysis (PCA) can be used for data dimensionality reduction. The PCA process is conducted by following the 4 steps listed below:

Step 1: input data and normalize the data to ensure that the data of the same attribute are on the same column vector.

Step 2: apply the principle of the principal component to find K principal components and obtain the feature vector and eigenvalue of the data sample.

Step 3: let the principal component vector set replace the original vector of the data sample set and arrange the principal components in order according to the size of the data sample information.

Step 4: according to the sorting result, when the accumulated contribution rate comes to 0.85, the data samples can reflect the main part of the original sample. The data samples contained what can better reflect the information amount of the data in the original sample and remove the principal component with a lower contribution rate to achieve the ultimate goal of data sample dimension reduction [

The support vector machine (SVM) is an advanced classification method introduced by Boser et al. [

By calculating the interval between the outer lines as

Using SVM for machine learning, the whole learning process is equivalent to finding the optimal value of the parameters. We introduce Lagrangian multipliers to solve this problem:

Introducing the Lagrangian multiplier, this is expressed as

Deriving the parameters

Then, it obtains

Maximizing the spacing between classification planes is essentially an improvement and optimization of the SVM’s generalization ability. By maximizing the classification plane spacing, the structural risk of the SVM is minimized, and the generalization ability of the algorithm is strengthened to meet the core idea of the SVM. In order to solve the problem in the above formula, the dual theory is introduced, and the classification problem is transformed into a dual problem:^{th} sample, and equation (

Then, the optimal classification function is finally found:

The test sample

Most of the samples classified are linear inseparable samples. Since the objective function of the dual function and the optimal classification function of the solution have only

The optimal classification function is transformed into

Suppose the population size is ^{th} wolf set in the search space is

The distance

In the above formula,

Grey wolf individual location updates are as follows:

In equations (

When the grey wolf individual calculates the distance and estimates the specific position of the prey,

The distance between the individual grey wolf and the prey is calculated by (

However, with the increase in the number of iterations, the grey wolf population will have a population difference in some areas of the search space. This phenomenon has a great negative impact on the optimization performance of the algorithm. Aiming at the limitations of the standard form of grey wolf algorithm, this paper introduces a differential evolution of the grey wolf algorithm [

The mutation operation of the differential evolution algorithm can significantly enhance the global search ability of the algorithm. The main point is that two different individuals in the arbitrarily selected population can differentially scale their position vectors, thereby obtaining a series of differential information and assigning it to another among individuals who are not mutated. The mutation operator needs a mutation operator. This algorithm refers to the adaptive mutation operator proposed by FAN. The introduced adaptive operator is

The above formula

The complete mutation operation is

In the variation operation formula (

If the individual resulting from the cross-variation of equation (

In equation (

The cross-operation of the differential evolution algorithm mainly improves the improvement effect of mutation data by exchanging related data elements of mutated individuals and unmutated individuals. The algorithm applies the basic cross-strategy of the differential evolution algorithm. The populations initialized by the optimal point set are equally divided into two groups according to the fitness value and are processed by the mutation operation, followed by the intersection of the elements. The specific formula is as follows:

Among them, CR represents the probability value of cross mutation and is the random generation number, and

Selection operation of differential evolution using greedy thought to select the next generation of grey wolf population; the operation mode is expressed by the following formula:

The penalty factor and kernel function parameters of the SVM are randomly selected by the algorithm. Under this condition, the classification accuracy of the algorithm is restricted [

Population crossing rule diagram.

The dataset is obtained from the monitoring information system of a coal mine which is transferring into an intelligent mine on the basis of IOTs (Internet of Things) and AI (artificial intelligence) in eastern China; the typical fault conditions and the normal running state of the belt conveyor are selected as the research objects. The six typical faults are belt slip and belt tear, belt deviation, motor failure, main belt overload, and belt fire accident [

Training set and test set partition table.

Type label | Fault type | Train set | Test set |
---|---|---|---|

Label 1 | Normal status | 20 | 8 |

Label 2 | Belt slip | 17 | 7 |

Label 3 | Belt tear | 8 | 3 |

Label 4 | Belt deviation | 15 | 6 |

Label 5 | Motor failure | 15 | 6 |

Label 6 | Belt overload | 10 | 4 |

Label 7 | Belt fire | 5 | 2 |

The 19 parameter indices such as motor power, motor temperature, belt speed, and bearing temperature are standardized, then notated by X1 to X19, and imported into SPSS software. Finally, the total variance between the output parameter indicators is explained, and the total variance is explained. The contribution rate of each principal component to the population can be arranged, the principal component whose total contribution rate of the principal component reaches 0.85 or above is selected, and the above main component is used instead of the original sample data for classification processing, thereby achieving the purpose of dimension reduction of the characteristic index.

The total variance interpretation chart output in the SPSS software is shown in Table

Interpretation of the total variance.

Component | Extraction sums of squared loadings | ||
---|---|---|---|

Total | Percentage of variance | Cumulation (%) | |

1 | 8.522 | 44.852 | 44.852 |

2 | 3.082 | 16.220 | 61.072 |

3 | 2.129 | 11.207 | 72.279 |

4 | 1.577 | 8.303 | 80.581 |

5 | 1.069 | 5.625 | 86.208 |

Extraction method: principal component analysis

The number of feature samples is imported into the fault diagnosis model, and 126 sets of samples after the dimension reduction using the PCA algorithm are selected. The training set and the test set are divided according to the ratio of the test set and the training set of about 3 : 1. Among them, 90 sets of training sets are used for the establishment of the multiclass fault diagnosis model and the optimization operation of SVM kernel parameters. The remaining 36 sets of data are used as test set samples [

Set the number of wolves to twenty and the maximum number of iterations is sixty. Choose RBF kernel function, the superparameter values range is

Training set classification identification.

Test set classification identification.

To verify the performance of the hybrid fault diagnosis model proposed in this paper, the fault classification efficiency is compared with GWO-SVM, PCA-SVM, and PCA-GWO-SVM. The specific setting of the control experiment is as follows: the data used in the GWO-SVM model are 19-dimensional feature sample data. Model parameters optimization method with a standard grey wolf optimizer (GWO) and the setting of the parameters in accordance with the setting of the hybrid grey wolf optimizer for the same settings: the PCA-GWO-SVM model was used to reduce the dimension of the data, and then it was imported into the standard grey wolf optimizer optimized SVM classifier for classification and recognition. The PCA-SVM model aims to reduce the dimension of sample data, after which the data are imported to the SVM model for classification, while the parameter of the model is set by experience. Table

Kernel parameter table.

Model building form | Penalty factor | Kernel function width |
---|---|---|

PCA-SVM | 2 | 0.05 |

GWO-SVM | 22.6545 | 12.4765 |

PCA-GWO-SVM | 36.9754 | 4.9578 |

PCA-HGWO-SVM | 25.3684 | 8.8765 |

As shown in Figures

PCA-SVM classification of the training sets.

Test set classification diagram.

As shown in Figures

GWO-SVM training set classification recognition map.

GWO-SVM test set classification identification.

As shown in Figure

Classification identification of the PCA-GWO-SVM training set.

Figure

Classification identification of the PCA-GWO-SVM test set.

Classification accuracy table of different models.

Model type | Train set accuracy (%) | Test set accuracy (%) | Operation time (s) |
---|---|---|---|

PCA-SVM | 78.89 (71/90) | 77.78 (28/36) | 0.965 |

GWO-SVM | 84.44 (76/90) | 80.55 (29/36) | 11.9546 |

PCA-GWO-SVM | 91.11 (82/90) | 91.67 (33/36) | 5.1547 |

PCA-HGWO-SVM | 97.78 (88/90) | 97.22 (35/36) | 4.4567 |

According to Table

At the same time, the data also showed that the classification accuracy of the PCA-GWO-SVM model was greatly improved compared with that of the former one, indicating that PCA does not only simplified the complexity of the fault model but also eliminated the redundant information of the model and further improved the accuracy of the model. Finally, to verify the effectiveness of the proposed hybrid model, the comparative analysis was conducted with several other methods for model verification. According to Table

In this paper, a hybrid diagnosis model is developed based on the support vector machine model, which combines principal component analysis and grey wolf optimizer to apply to the fault classification of belt conveyor. Aiming at the limitations of the standard grey wolf optimizer, we proposed a method of hybrid wolf optimizer for parameter optimization. The experimental results show that the fault diagnosis model proposed in this paper has a higher overall diagnosis and recognition efficiency compared with the single method model, and its fault classification accuracy is up to 97.22%, which could help to improve the reliability of the belt transport system of the coal mine.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

This work was supported by the National Key R&D Program of China (Grant no. 2017YFC0804408).