Mechanical Fault Prediction Based on Event Knowledge Graph
Li He
1
, Liang Zhang
2,
and Wei Yan
1,
1
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, Shandong, China
2
Institute of Frontier and Interdiscriplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE),
Shandong University, Qingdao 266237, Shandong, China
Keywords:
Event Knowledge Graph, Mechanical Fault, Convolutional Neural Networks, Fault Diagnosis, Fault
Prediction.
Abstract:
Currently, the majority of diagnoses in the field of mechanical faults are performed by experts or expert
systems, which require domain experts to guide the completion while having subpar and limited portability.
Consequently, we analyzed the current situation of rolling bearing fault, event knowledge graph and convo-
lutional neural network, explored the intelligent fault diagnosis and prediction technology of rolling bearing,
introduced event knowledge graph and convolutional neural network in rolling bearing fault diagnosis, pro-
vided support for fault diagnosis and prediction, enhanced the accuracy of fault diagnosis and prediction, and
enabled the predictive of complex mechanical equations.
1 INTRODUCTION
Despite the fact that fault diagnosis technology is an
applied and marginal field, the emphasis is transi-
tioning toward information technology, digitalization,
and artificial intelligence as a result of technologi-
cal advancements. The sensitivity and complexity of
the large-scale mechanical apparatus have increased.
Moreover, calamitous incidents caused by mechanical
equipment malfunctions have become more prevalent
in recent years as a result of the significant expansion
in the complexity of mechanical equipment. There-
fore, the use of fault diagnosis and predictive tech-
niques for predictive maintenance of apparatus and
equipment is crucial for maintaining the equipment’s
operation stability and preventing losses due to out-
ages.
Theoretical foundations for fault diagnosis tech-
nologies include modern control theory, computer en-
gineering, mathematical statistics, fuzzy set theory,
signal processing, pattern recognition, etc. The objec-
tive of fault diagnosis is to determine the fault’s char-
acteristic description and to detect and isolate the fault
based on the various (measurable or unmeasurable)
quantities in the system, or some of them, exhibiting
characteristics distinct from their normal state when a
fault occurs(An, 2008).In the initial stages, manual di-
agnosis is used. However, the traditional technology
*Corresponding authors.
of fault diagnosis has been unable to satisfy the actual
requirements for the operation of complex mechani-
cal equipment. The prediction model is a crucial com-
ponent of the technology for predicting mechanical
faults. Current fault prediction models include curve
fitting models, filtering models, time series models,
grey models, artificial neural network models, and
fuzzy models.
Knowledge Graph (KG) is a popular form of
knowledge representation, published by Google in
2012. It focuses on entities and their relationships,
thus representing static knowledge. And existing
knowledge graphs in the field of mechanical fault
are frequently static graph, where the degree of fault
is inferred by associating semantic information with
the data(Yan et al., 2022). However, the world con-
tains a vast quantity of event information that conveys
dynamic and procedural knowledge, making event-
centric knowledge representations such as the Event
Knowledge Graph (EKG) essential.
In this paper, in contrast to the conventional ap-
proach of using static knowledge graph, we use
Event-Ontology(Han et al., 2007) combined with sig-
nal data from mechanical fault data sets to construct
event knowledge graph. We also utilize the data in the
event knowledge graph to construct a convolutional
neural network model for fault diagnosis and predic-
tion functions, and then update the event knowledge
graph with the results of the diagnosis and prediction.
164
He, L., Zhang, L. and Yan, W.
Mechanical Fault Prediction Based on Event Knowledge Graph.
DOI: 10.5220/0012178200003598
In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 2: KEOD, pages 164-173
ISBN: 978-989-758-671-2; ISSN: 2184-3228
Copyright © 2023 by SCITEPRESS Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
This knowledge graph can be utilized for predictive
maintenance of machinery and equipment to avoid or
minimize downtime, thereby ensuring that machinery
and equipment can be utilized for as long as possible.
2 RELATED WORK
Currently, mechanical fault prediction (MFP) and
diagnosis are usually implemented independently
by knowledge graphs or convolutional neural net-
works. Laibin Zhang et al.(Zhang et al., 2007) pro-
posed a multidimensional fault characteristic parame-
ter model for rotating parts for the purpose of mechan-
ical fault diagnosis technology. Specifically, a fuzzy
gray optimal prediction method is proposed to predict
short-term faults. The method is able to build a pre-
diction model with a minimum of four data and can
effectively handle the nonlinearity of the prediction
data. Shi Yu et al.(Yu Shi, 2022) proposed a model
for fault diagnosis using a knowledge graph and fuzzy
inference. By constructing a knowledge graph of the
mechanical fault domain, a fuzzy ontology, and com-
municating the knowledge graph and fuzzy ontology
in a mapping manner, the equipment status can be di-
agnosed using fuzzy reasoning to derive fault fuzzy
values. Although the fuzzy inference model can also
accomplish the diagnosis and prediction of mechani-
cal faults, its implementation requires the manual or
semi-automatic construction of a fuzzy inference rule
base and the setting of thresholds for the addition
of inference labels. The rules in the rule repository
should not have an excessive number of parameters,
as this would reduce the efficacy of inference.
Although Google introduced the Knowledge
Graph concept formally in 2012, it has been under
development since the Semantic Web was first pro-
posed in 1960. Early knowledge bases, open knowl-
edge graphs, Chinese common knowledge graphs,
and domain knowledge graphs are the various types
of knowledge graphs(Tian et al., 2021). The event
knowledge graph is a dynamic knowledge graph, as
opposed to a static knowledge graph, and the data is
frequently updated and has a certain timeliness(Zhao
et al., 2022); thus, it is difficult to achieve improved
results when using inference methods such as fuzzy
inference for prediction or diagnosis. Typically, event
knowledge graphs employ an ontology-based con-
struction process for knowledge modelling, wherein
the top-level representation model of event knowl-
edge graphs is developed first, followed by the re-
finement of supporting examples. This method is
comparable to that of static knowledge graphs. And
the method proposed by Zhao Xiaojun et al.(Zhao
et al., 2022) for constructing a dynamic military do-
main graph by processing dynamic data according to
data update frequency and usage frequency can better
satisfy the requirements of a domain dynamic knowl-
edge graph for high quality and timeliness.
Knowledge graph is a significant infrastructure for
AI technology and is essential for computers to ac-
complish human-like inference and prediction capa-
bilities. Compared with shallow models, deep learn-
ing has more obvious advantages in both feature ex-
traction and modeling. Deep learning is better at min-
ing abstract feature representations from original data,
and these feature representations usually have bet-
ter generalization ability. From AlexNet(Krizhevsky
et al., 2012), VGGnet(Simonyan and Zisserman,
2014), and GoogLeNet(Szegedy et al., 2015), which
participated in the ILSVRC competition and won, it is
clear that convolutional neural networks are the undis-
puted leaders in computer vision and have excellent
generalization. Therefore, it is reasonable to assume
that convolutional neural networks can contribute new
concepts and techniques to the field of mechanical
fault diagnosis.
By taking advantage of convolutional neural net-
works and building CNN models that act directly
on time-domain signals, features useful for diagnosis
can be learned automatically without manual extrac-
tion(Zhang, 2017). Using the upper channel CNN to
extract feature information from the vibration signal
and the lower channel Bidirectional Gated Recurrent
Unit(BiGRU) network to extract the temporal features
of the vibration signal from both positive and negative
directions enables the diagnosis of faults by combin-
ing the benefits of both network models.
Most implementations of the fault prediction func-
tion in the aforementioned papers use CNNs for pre-
diction or only KGs for inference, whereas by com-
bining the prediction capability of CNNs with the
event attributes of EKGs, we are able to more effec-
tively predict mechanical faults.
3 METHODOLOGY
The methodology framework for the mechanical fault
prediction model based on event knowledge graph
and deep convolutional neural network is depicted in
Figure 1.
Four major components comprise the model: the
input layer, the EKG layer, the Deep Convolu-
tion Neural Networks with Wide First-Layer Kernels
(WDCNN) layer, and the output layer. The model
input consists of as much data as feasible, including
fan end and drive end vibration data, motor revolu-
Mechanical Fault Prediction Based on Event Knowledge Graph
165
Figure 1: Framework of the prediction system.
tions per minute (RPM), and time series data. The
EKG layer then extracts relevant information from the
data to establish entities and their relationships. By
reading the EKG layer’s data, the WDCNN layer per-
forms fault prediction using the pre-trained WDCNN
model. When a fault is predicted to be possible, a
new entity is added to the EKG, and its relationships
with other entities, such as the (entity-fault type) re-
lationship, are established; if no fault is predicted, the
corresponding entity’s attributes and relationships are
merely updated. The output layer obtains the output
by reading the attributes and inter-entity relationships
of the altered or added entities in the EKG layer.
3.1 EKG Layer
In this paper, we constructed the event knowledge
graph based on the seven-step method(Noy and
Mcguiness, 1995) and the 6H method(Han et al.,
2007). At present, the more widely used knowledge
graph construction methods are: IDEF-5 method,
Skeletal Methontolody, TOVE method, seven-step
method(Noy and Mcguiness, 1995) and Methontol-
ogy method(Fern
´
andez-L
´
opez et al., 1997). In the
construction of domain ontologies, the seven-step
method and the NeOn Methontology method are now
utilized more frequently. Below are brief explanations
of the two aforementioned methods: Currently, the
most popular methods for building knowledge graphs
are the IDEF-5 method, Skeletal Methontolody,
TOVE method, seven-step method(Noy and Mcgui-
ness, 1995), and Methontology method(Fern
´
andez-
L
´
opez et al., 1997). More frequently, the seven-step
method and the NeOn Methontology method are em-
ployed in the construction of domain ontologies to-
day. Detailed explanations of the two aforementioned
methodologies are provided below.
(i) Seven-Step Method (Noy and Mcguiness, 1995):
The Stanford University School of Medicine devised
this method, which is primarily used for the construc-
tion of domain ontologies. The seven stages of the
method are: (1) identifying the domain of expertise
and scope of the ontology; (2) investigating the pos-
sibility of utilizing existing ontologies; and (3) devel-
oping the ontology. (3) listing the key terms in the
ontology; (4) defining classes and class hierarchies;
(5) defining the attributes of classes; (6) defining the
constraints of the attributes; (7) generating instances.
(ii) NeOn Methontology Method (Gomez-Perez
and Su
´
arez-Figueroa, 2009): The core idea of
NeOn methodology is to integrate ontologies from
different domains into a unified knowledge graph.
It is constructed using a ”divide-and-conquer” ap-
proach, in which the overall problem to be addressed
is decomposed into subproblems, and solutions to the
overall problem (i.e., the development of the ontology
network) are obtained by combining the solutions to
the subproblems.
Event-Ontology is defined based on fundamental
factors which describe an event. That means the 6H of
’who’, ’what’, ’where’, and ’how’ are represented as
properties of an event. The property value of ”who”
is the subject of an event, while the property value
of ”how” is the subject’s action or the event’s con-
tent. ”What” refers to the substance of an event, while
”where” and ”when” indicate the event’s location and
time, respectively.(Han et al., 2007).
EKG gave to construct entities and relationships
between entities by extracting as much data infor-
mation as possible in the dataset, such as vibration
signal data, fault occurrence locations, fault types,
etc. This paper refered to the knowledge graph de-
veloped by Liu Xin(Liu, 2019) of Beijing University
of Posts and Telecommunications using the improved
seven-step methodology. Because mechanical fault
event knowledge graph is a type of domain knowledge
graph, and in order to share a common understand-
ing of the information structure and make the domain
knowledge reusable, it is typically necessary to deter-
mine whether there are existing ontologies that can be
reused before developing the ontology. The structure
Liu proposed is enhanced on the basis of the ontology
class hierarchy diagram of this knowledge graph to
emphasize the features of the event knowledge graph.
By deleting class structures that are not relevant to this
paper and adding classes corresponding to the infor-
mation extracted above, an ontology class hierarchy
that better highlights the characteristics of the event
KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development
166
knowledge graph is constructed.
3.2 WDCNN Layer
The WDCNN layer determines the structure of the
model and the parameters of each layer by extract-
ing the vibration signal data from the established
knowledge map of mechanical failure events and sev-
eral preliminary experiments, thus establishing a WD-
CNN model with the function of diagnosis and pre-
diction of mechanical faults, whose diagnostic func-
tion can recognize ten types of failures. As a nor-
mal Convolution Neural Network(CNN), WDCNN
also has a five-layer structure , which are input layer,
convolution layer, pooling layer(or sampling layer),
full connection layer and output layer. Among them,
the convolution layer and pooling layer are set alter-
nately. In contrast to conventional convolution neural
networks, however, WDCNN have a large convolu-
tion kernel in the first layer and smaller convolution
kernels in subsequent layers. Due to the small num-
ber of convolution kernel parameters, WDCNN has
a deeper network and can suppress overfitting, so it
has a better expressive ability compared with ordinary
CNNs. We can also ascertain the applicability of the
model’s findings by examining the displayed model
assessment curves. It implements its prediction func-
tion by determining whether mechanical equipment is
likely to malfunction based on a threshold value of the
difference between predicted and actual values.
3.3 Output
In the output layer, mechanical fault diagnosis and
prediction results are obtained by retrieving the up-
dated EKG output results, which can be done by
clicking on the visualization interface or using query
statements.
We implemented the query function of this me-
chanical fault event knowledge graph in Neo4j. Be-
cause of its ability to visualize the entities we create
and the relationships between them compared to other
databases. In addition, Neo4j can be queried with
Cypher statements or simple clicks, which makes it
accessible to people who do not have knowledge of
database queries. Cypher’s fundamental syntax com-
prises of four distinct sections, each with a unique
rule: (1) Start: identify the starting node in the graph;
(2) Match: match the graph pattern to locate sub-
graphs of the desired data; and (3) Where: filter the
data based on certain criteria. data; (4) Return: de-
liver the desired results.
4 EXPERIMENTAL SETUP
In this section, we discuss all the experimental envi-
ronments we considered to perform the Event Knowl-
edge Graph of Mechanical Fault Prediction. In the
following subsections we discuss: (i) the data set used
to build and test; (ii) the metrics used to assess perfor-
mances; and (iii) the implementation aspects of the
proposed EKG of MFP model.
4.1 Data Sets Description
The experimental dataset used for this paper is the
rolling bearing dataset published by the Rolling Bear-
ing Data Center at Case Western Reserve Univer-
sity(CWRU, 2023). This dataset is presently recog-
nized as the standard dataset for fault diagnosis of
rolling bearings worldwide. The investigations in this
paper therefore make use of the Normal Baseline, 12k
Drive End Bearing Fault, and 12k Fan End Bearing
Fault data sets from the CWRU dataset. We used the
data in Normal Baseline and 12k Drive End Bearing
Fault to construct the event knowledge graph, then
train and construct WDCNN using the event knowl-
edge graph’s data, and finally use the data in 12k Fan
End Bearing Fault to test the generalizability of the
established EKG of MFP.
4.2 Data Sets Pre-Processing
The initial data set is stored in Matlab (*.mat) files,
each file contains fan and drive end vibration data in
addition to motor speed.
The experimental object of this paper is a deep
groove ball bearing SKF6205 model drive end bear-
ing. The faulty bearing is machined by EDM with a
system sampling frequency of 12kHz, and it is sepa-
rated into three categories based on its damaged parts:
inner race, outer race, and ball, and four categories
based on its fault diameters: 0.007, 0.014, 0.021, and
0.028 inches. However, due to the lack of outer race
damage data for the 0.028 inch diameter portion, the
0.007, 0.014, and 0.021 damage diameters were se-
lected, for a total of 3×3 fault states. As outer race
damage is a stationary damage, the location of the
fault in relation to the bearing load zone has a direct
impact on the vibratory response of the motor/bearing
system. The outer race fault location chosen for this
research is in the 6 o’clock direction (directly within
the load zone). The data sets used are shown in the
Table 1 and Table 2.
Then, the data set is randomly partitioned into
train, valid, and test, and the partitioning ratio is set
to 0.7: 0.1: 0.2 so that training and test set samples
Mechanical Fault Prediction Based on Event Knowledge Graph
167
Table 1: Normal Baseline Data.
Motor Load (HP) Approx. Motor Speed (rpm) Normal Baseline Data
0 1797 97.mat
1 1772 98.mat
2 1750 99.mat
3 1730 100.mat
do not overlap. In each experiment, 1024 data are uti-
lized for fault diagnosis.
In addition, there are 10 fault categories that have
been artificially specified, corresponding to 1 fault-
free status and 9 fault statuses with three fault loca-
tions (inner race, ball, outer race) and three fault di-
ameters (0.007, 0.014, 0.021) in two combinations.
Table 3 lists the types corresponding to these fault sta-
tus.
Due to the fact that the subsequent query includes
bearing location, motor load horsepower (HP), mo-
tor speed, bearing operation status, fault diameter (in
fault status), fault occurrence location (in fault status),
and fault name, etc., the corresponding data columns
are added to the data set and output as excel (.csv)
files.
Before conducting fault prediction, it is necessary
to obtain new data. In this paper, we utilize the data in
the 12k DriveEndBearingFaultData file with the split
ratio set to [train: valid: test]=[0.1: 0.1: 0.80], pre-
sume the data in the test section to be recently col-
lected data, and transform the imported data into a
format acceptable by the model input layer.
4.3 Metrics
The purpose of this paper is to accomplish supervised
diagnosis and prediction of mechanical faults. Ac-
curacy and loss curves are frequently used as crucial
metrics for evaluating the efficacy of machine learn-
ing models, and loss curves can reflect the dynamic
trend of network training. To evaluate the diagnostic
and predictive quality of the WDCNN model devel-
oped in this paper for bearing faults, we observe the
loss curve to determine whether the model converges
and fits, and the acc curve to determine the model’s
applicability for bearing fault prediction.
4.4 Implementation Details
The construction of EKG and the establishment of
WDCNN are carried out using the methodologies de-
scribed above.
EKG Construction: First, we analyzed the data in
the CWRU bearing fault dataset and identify the key
terms in the ontology; then, we defined the classes and
the class hierarchy based on these terms. We used a
combination of top-down and bottom-up approaches
and referred to the ontology class hierarchy of the
knowledge graph created by Xin Liu of Beijing Uni-
versity of Posts and Telecommunications(Liu, 2019)
to define classes and class hierarchies in the EKG.
Analysis of the CWRU rolling bearing dataset from
the bottom-up layer provides access to the important
terms in the field of rolling bearing faults: bearing
operating part, bearing physical component, bearing
fault type, bearing operating status, bearing operating
conditions, bearing protection methods, etc., which
can all be used as concepts extracted from the top-
down layer to construct the ontology. The bottom
stratum of data contains bearing fault diameters, time
series data, fault varieties, etc. Therefore, the bearing
operation status can be divided into two categories:
normal and abnormal, with the fault status subdivided
further into fault vibration signal, fault diameter, etc.
In summary, the class hierarchy can be deduced as fol-
lows: A rolling bearing includes several categories of
operating part, physical component, operating status,
faults, and bearing protection methods. The operating
status contains subcategories for normal state and ab-
normal state. The normal state subcategory contains
normal state signal data, while the abnormal state sub-
category contains fault signal data and specific fault
type. The operating condition also includes motor
load horsepower and motor speed.
Then, to facilitate subsequent query functions and
data updates, we specify the class’s data attributes and
relational attributes, upon which we define the con-
straints for each attribute. Instances are subsequently
created in the Neo4j graph database to conclude the
construction of the bearing fault event knowledge
graph.
WDCNN Establishment: The purpose of establish-
ing a convolutional neural network in this paper is
to realize the diagnosis and prediction of mechani-
cal faults, using tensorflow in python to establish the
initial CNN and then selecting the WDCNN with su-
perior performance based on a series of preliminary
experiments. The WDCNN consists of ve convolu-
tional layers, five pooling layers, one full connection
and one Softmax layer. Following are the parame-
ters of the convolutional neural network: The size of
the first convolutional kernel is 64×11 with a 16×11
stride size, the size of the remaining convolutional
layers is 3×11 with a 2×11 stride size, and the size of
each of the five pooling layers is 2×11 with a 2×11
stride size. In addition, the Softmax layer is config-
ured to generate 10 outputs for 10 bearing fault states.
The parameters of the WDCNN model used in the ex-
periments are shown in Table 4.
Moreover, the Adadelta optimization algo-
KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development
168
Table 2: 12k Drive End Bearing Fault Data.
Fault Diameter Motor Load (HP) Approx. Motor Speed (rpm) Inner Race Ball
Outer Race
Position Relative to Load Zone (Load Zone Centered at 6:00)
Centered
@6:00
Centered
@3:00
Centered
@12:00
0.007” 0 1797 105.mat 118.mat 130.mat 144.mat 156.mat
1 1772 106.mat 119.mat 131.mat 145.mat 158.mat
2 1750 107.mat 120.mat 132.mat 146.mat 159.mat
3 1730 108.mat 121.mat 133.mat 147.mat 160.mat
0.014” 0 1797 169.mat 185.mat 197.mat * *
1 1772 170.mat 186.mat 198.mat * *
2 1750 171.mat 187.mat 199.mat * *
3 1730 172.mat 188.mat 200.mat * *
0.021” 0 1797 209.mat 222.mat 234.mat 246.mat 258.mat
1 1772 210.mat 223.mat 235.mat 247.mat 259.mat
2 1750 211.mat 224.mat 236.mat 248.mat 260.mat
3 1730 212.mat 225.mat 237.mat 249.mat 261.mat
0.028” 0 1797 3001.mat 3005.mat * * *
1 1772 3002.mat 3006.mat * * *
2 1750 3003.mat 3007.mat * * *
3 1730 3004.mat 3008.mat * * *
Table 3: 10 types of fault names.
diameter\position inner race ball outer race
0.007 1 2 3
0.014 4 5 6
0.021 7 8 9
normal (no fault) 0
Table 4: Parameters of WDCNN.
No. Network layers
kernel size
/stride size
Number of kernels
output
(width × depth)
1 Convolutional layer1 64×1/16×1 16 128×116
2 Pooling layer 2×11/2×11 16 64×116
3 Convolutional layer 3×11/1×11 32 64×132
4 Pooling layer 2×11/2×11 32 32×132
5 Convolutional layer 3×11/1×11 64 32×164
6 Pooling layer 2×11/2×11 64 16×164
7 Convolutional layer 3×11/1×11 64 16×164
8 Pooling layer 2×11/2×11 64 8×164
9 Convolutional layer 3×11/1×11 64 6×164
10 Pooling layer 2×11/2×11 64 3×164
11 full connection 100 1 100×11
12 Softmax 10 1 10
rithm(Zeiler, 2012) has been used to revise the
weights in this paper. The Adadelta optimization
algorithm is an improvement of the Adagrad op-
timization algorithm, in which the exponentially
weighted average of the squares of the gradients is
used instead of the sum of squares of all gradients
for each dimension, thereby avoiding the problem
that the update magnitude gradually tends to zero in
the late training period; the exponentially weighted
average of the squares of the updates dynamically
replaces the global scalar learning rate, thereby
avoiding truncation.
5 RESULT AND DISCUSSION
In this section, we discuss the query function imple-
mented by the bearing fault EKG and the performance
of WDCNN in terms of loss and accuracy of fault di-
agnosis and prediction.
5.1 Query Functions of Bearing Fault
EKG
In this paper, we present four kinds of most prevalent
query functions based on the Cypher query pattern
and the visual interface provided by Neo4j. Analysis
of the constructed event knowledge graph of bearing
fault can identify the subclass or subclasses of entities
that the entity being queried falls under. By pressing
on nodes, the data attributes associated with nodes
and the relationships between nodes can be queried
based on the visual interface.
Rolling Bearing Operation Status and Physical
Components and Other Parameters: This query
searches for entities and their vibration signal data/
fault vibration signal data data attributes that belong
to the Normal or Abnormal subclasses of the oper-
ating status class. Figure 2 depicts the query result
when the operation status is normal, while Figure 3
depicts the query result when the operation status is
abnormal.
Rolling Bearing Fault Type and Fault Position:
The query is for the ”where” attribute in the event
Mechanical Fault Prediction Based on Event Knowledge Graph
169
Figure 2: Query result of normal status.
Figure 3: Query result of abnormal status.
KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development
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Figure 4: Query result of fault type and position.
knowledge graph, and there are two querying meth-
ods: (i) directly querying the fault type and returning
the fault type node directly; (ii) transforming it into a
query for the ”happened at” relationship query, which
returns the type of fault that occurred on the physi-
cal component. The query result for the fault type
”fault 7” is illustrated in Figure 4.
Rolling Bearing Fault Type and Solution: The
query aims to identify a solution based on the type
of defect, which can be translated into a query for the
”solution is” relationship for the starting node speci-
fied. Take the fault type ”fault 5” as an example, the
result of its fault solution query is shown in Figure 5.
Figure 5: Query result of fault type and solution.
Rolling Bearing Physical Components and Main-
tenance Methods: The query intends to identify
the corresponding maintenance solution based on the
physical components of the rolling bearing, i.e., spec-
ify the starting node as a physical component entity
and locate the end node with the relationship ”main-
tenance method is”. Figure 6 depicts the result of the
maintenance method inquiry regarding the inner race
malfunction.
Figure 6: Query result of physical components and mainte-
nance methods.
5.2 WDCNN Evaluation
The dataset is trained using a deep convolutional neu-
ral network constructed according to Section 4.5, with
the learning rate of Adadelta set to 1.0 and the number
of training epochs set to 500. Figure 7 and Figure 8
depict the loss and accuracy curves for each training
and test, respectively.
With a decent fit, the recognition rate of the con-
volutional neural network exceeds 99.8% on the train-
ing dataset and 97% on the test dataset, as depicted
in the figures. The experimental results demonstrate
that WDCNN has a strong ability to recognize the test
sample data.
The WDCNN model established in the preceding
section is applied to assess the test data and calculate
the average loss and accuracy values for the newly
Mechanical Fault Prediction Based on Event Knowledge Graph
171
Figure 7: Loss curve of training.
Figure 8: Accuracy curve of training.
collected data. If the average loss in the test history
is greater than 0.2 or the accuracy is less than 0.5,
then the operating status of the bearing is ”Abnormal”
if the final average loss is greater than 0.2 or the ac-
curacy is less than 0.5 based on the test history, and
”Normal” otherwise. The prediction effect is illus-
trated in Figures 9 and 10.
Figure 9: Loss curve of prediction.
Then, we evaluate the function of the constructed
Figure 10: Accuracy curve of prediction.
model to create new nodes based on the prediction re-
sults, using ”normal” as an example of a prediction
result. We constructed a new node with the name
”new normal” under the class ”normal type” in the
diagram. Establish the ”type of” relationship between
this node and the ”Normal” entity within the ”operat-
ing status” class. Establish the ”fault is” relationship
between this node and the ”fault 0” entity within the
”fault details” class. The newly constructed node is
queried in the manner depicted in Figure 11. Simi-
lar to the status of ”Normal”, when the running state
is ”Abnormal”, create a new node with the name
”new abnormal” under the class ”abnormal type” in
the diagram. Establish the ”type of” relationship be-
tween this node and the ”Abnormal” entity within the
class ”operating status”.
Figure 11: New node of predicted results.
6 CONCLUSION
In this paper, we focused on the analysis of faults in
rolling bearings, event knowledge graphs, and the cur-
rent state of convolutional neural networks, as well as
fault diagnosis and prediction techniques for rolling
bearings. Based on the characteristics of the mechan-
KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development
172
ical fault field and the data collected from the CWRU
rolling bearing data center, we extract the relation-
ship between entities and entities using a seven-step
process, store the obtained entities and inter-entity re-
lationships in the Neo4j graph database to complete
the construction of the bearing fault event knowledge
graph, and implement the four types of visual query
functions based on the constructed event kdb. Then,
we construct a deep convolutional neural network
with a wide convolutional kernel on the first layer
in order to diagnose the operational status of rolling
bearings. The experimentally established WDCNN
model has an accuracy rate of greater than 99.8% on
the training dataset and greater than 97.3% on the test
dataset. Finally, we predict bearing faults by com-
bining an event knowledge graph with a deep con-
volutional neural network. By acquiring newly col-
lected data during rolling bearing operation, setting
fault thresholds, and applying WDCNN to predict and
analyze them, new nodes and their relationships are
added to the event knowledge graph.
In rolling bearing fault diagnosis, the introduction
of the event knowledge graph and convolutional neu-
ral network provides support for fault diagnosis and
prediction, enhances the accuracy of fault diagnosis
and prediction, and reduces the need for expertise and
expert system domain knowledge.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Sci-
ence Foundation of China (62002207, 62072290,
62073201), the Shandong Provincial Natural Sci-
ence Foundation (ZR2022YQ05, ZR2020MA102)
and Shandong Provincial Key Laboratory for Novel
Distributed Computer Software Technology.
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