Visible to the public Biblio

Filters: Keyword is fault diagnosis  [Clear All Filters]
2021-03-16
Li, M., Wang, F., Gupta, S..  2020.  Data-driven fault model development for superconducting logic. 2020 IEEE International Test Conference (ITC). :1—5.

Superconducting technology is being seriously explored for certain applications. We propose a new clean-slate method to derive fault models from large numbers of simulation results. For this technology, our method identifies completely new fault models – overflow, pulse-escape, and pattern-sensitive – in addition to the well-known stuck-at faults.

2021-03-09
Herrera, A. E. Hinojosa, Walshaw, C., Bailey, C..  2020.  Improving Black Box Classification Model Veracity for Electronics Anomaly Detection. 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). :1092–1097.
Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models.
2021-03-01
Golagha, M., Pretschner, A., Briand, L. C..  2020.  Can We Predict the Quality of Spectrum-based Fault Localization? 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). :4–15.
Fault localization and repair are time-consuming and tedious. There is a significant and growing need for automated techniques to support such tasks. Despite significant progress in this area, existing fault localization techniques are not widely applied in practice yet and their effectiveness varies greatly from case to case. Existing work suggests new algorithms and ideas as well as adjustments to the test suites to improve the effectiveness of automated fault localization. However, important questions remain open: Why is the effectiveness of these techniques so unpredictable? What are the factors that influence the effectiveness of fault localization? Can we accurately predict fault localization effectiveness? In this paper, we try to answer these questions by collecting 70 static, dynamic, test suite, and fault-related metrics that we hypothesize are related to effectiveness. Our analysis shows that a combination of only a few static, dynamic, and test metrics enables the construction of a prediction model with excellent discrimination power between levels of effectiveness (eight metrics yielding an AUC of .86; fifteen metrics yielding an AUC of.88). The model hence yields a practically useful confidence factor that can be used to assess the potential effectiveness of fault localization. Given that the metrics are the most influential metrics explaining the effectiveness of fault localization, they can also be used as a guide for corrective actions on code and test suites leading to more effective fault localization.
2020-12-01
Sunny, S. M. N. A., Liu, X., Shahriar, M. R..  2018.  Remote Monitoring and Online Testing of Machine Tools for Fault Diagnosis and Maintenance Using MTComm in a Cyber-Physical Manufacturing Cloud. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :532—539.

Existing systems allow manufacturers to acquire factory floor data and perform analysis with cloud applications for machine health monitoring, product quality prediction, fault diagnosis and prognosis etc. However, they do not provide capabilities to perform testing of machine tools and associated components remotely, which is often crucial to identify causes of failure. This paper presents a fault diagnosis system in a cyber-physical manufacturing cloud (CPMC) that allows manufacturers to perform diagnosis and maintenance of manufacturing machine tools through remote monitoring and online testing using Machine Tool Communication (MTComm). MTComm is an Internet scale communication method that enables both monitoring and operation of heterogeneous machine tools through RESTful web services over the Internet. It allows manufacturers to perform testing operations from cloud applications at both machine and component level for regular maintenance and fault diagnosis. This paper describes different components of the system and their functionalities in CPMC and techniques used for anomaly detection and remote online testing using MTComm. It also presents the development of a prototype of the proposed system in a CPMC testbed. Experiments were conducted to evaluate its performance to diagnose faults and test machine tools remotely during various manufacturing scenarios. The results demonstrated excellent feasibility to detect anomaly during manufacturing operations and perform testing operations remotely from cloud applications using MTComm.

2020-11-02
Zhang, Z., Xie, X..  2019.  On the Investigation of Essential Diversities for Deep Learning Testing Criteria. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). :394–405.

Recent years, more and more testing criteria for deep learning systems has been proposed to ensure system robustness and reliability. These criteria were defined based on different perspectives of diversity. However, there lacks comprehensive investigation on what are the most essential diversities that should be considered by a testing criteria for deep learning systems. Therefore, in this paper, we conduct an empirical study to investigate the relation between test diversities and erroneous behaviors of deep learning models. We define five metrics to reflect diversities in neuron activities, and leverage metamorphic testing to detect erroneous behaviors. We investigate the correlation between metrics and erroneous behaviors. We also go further step to measure the quality of test suites under the guidance of defined metrics. Our results provided comprehensive insights on the essential diversities for testing criteria to exhibit good fault detection ability.

2020-10-14
Song, Yufei, Yu, Zongchao, Liu, Xuan, Tian, Jianwei, CHEN, Mu.  2019.  Isolation Forest based Detection for False Data Attacks in Power Systems. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :4170—4174.
Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.
2020-10-12
Jharko, Elena, Promyslov, Vitaly, Iskhakov, Andrey.  2019.  Extending Functionality of Early Fault Diagnostic System for Online Security Assessment of Nuclear Power Plant. 2019 International Russian Automation Conference (RusAutoCon). :1–6.

The new instrumentation and control (I&C) systems of the nuclear power plants (NPPs) improve the ability to operate the plant enhance the safety and performance of the NPP. However, they bring a new type of threat to the NPP's industry-cyber threat. The early fault diagnostic system (EDS) is one of the decision support systems that might be used online during the operation stage. The EDS aim is to prevent the incident/accident evolution by a timely troubleshooting process during any plant operational modes. It means that any significative deviation of plant parameters from normal values is pointed-out to plant operators well before reaching any undesired threshold potentially leading to a prohibited plant state, together with the cause that has generated the deviation. The paper lists the key benefits using the EDS to counter the cyber threat and proposes the framework for cybersecurity assessment using EDS during the operational stage.

2020-10-06
Zaman, Tarannum Shaila, Han, Xue, Yu, Tingting.  2019.  SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :515—526.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

2020-10-05
Lago, Loris Dal, Ferrante, Orlando, Passerone, Roberto, Ferrari, Alberto.  2018.  Dependability Assessment of SOA-Based CPS With Contracts and Model-Based Fault Injection. IEEE Transactions on Industrial Informatics. 14:360—369.

Engineering complex distributed systems is challenging. Recent solutions for the development of cyber-physical systems (CPS) in industry tend to rely on architectural designs based on service orientation, where the constituent components are deployed according to their service behavior and are to be understood as loosely coupled and mostly independent. In this paper, we develop a workflow that combines contract-based and CPS model-based specifications with service orientation, and analyze the resulting model using fault injection to assess the dependability of the systems. Compositionality principles based on the contract specification help us to make the analysis practical. The presented techniques are evaluated on two case studies.

2020-08-24
LV, Zhining, HU, Ziheng, NING, Baifeng, DING, Lifu, Yan, Gangfeng, SHI, Xiasheng.  2019.  Non-intrusive Runtime Monitoring for Power System Intelligent Terminal Based on Improved Deep Belief Networks (I-DBN). 2019 4th International Conference on Power and Renewable Energy (ICPRE). :361–365.
Power system intelligent terminal equipment is widely used in real-time monitoring, data acquisition, power management, power distribution and other tasks of smart grid. The power system intelligent terminal can obtain various information of users and power companies in the power grid, but there is still a lack of protection means for the connection and communication process of the terminal components. In this paper, a novel method based on improved deep belief network(IDBN) is proposed to accomplish the business-level security monitoring and attack detection of power system terminal. A non-intrusive business-level monitoring platform for power system terminals is established, which uses energy metering intelligent terminals as an example for non-intrusive data collection. Based on this platform, the I-DBN extracts the spatial and temporal attack characteristics of the external monitoring data of the system. Some fault conditions and cyber attacks of the model have been simulated to demonstrate the effectiveness of the proposed detection method and the results show excellent performance. The method and platform proposed in this paper can be extended to other services in the power industry, providing a theoretical basis and implementation method for realizing the security monitoring of power system intelligent terminals from the business level.
2020-08-03
Huang, Xing-De, Fu, Chen-Zhao, Su, Lei, Zhao, Dan-Dan, Xiao, Rong, Lu, Qi-Yu, Si, Wen-Rong.  2019.  Research on a General Fast Analysis Algorithm Model for Pd Acoustic Detection System: The Software Development. 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :671–675.
At present, the AE method has the advantages of live measurement, online monitoring and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. In this paper, development of a data processing software for PD acoustic detection based on a general fast analysis algorithm model is introduced. With considering the signal flow chart of current acoustic detection system widely used in operation and maintenance of power system equipments, the main function of the developed PD AE signals analysis software was designed, including the detailed analysis of individual data file, identification with phase compensation based on 2D PRPD histograms, batch processing analysis of data files, management of discharge fingerprint library and display of typical defect discharge data. And all of the corresponding developed software pages are displayed.
Si, Wen-Rong, Fu, Chen-Zhao, Gao, Kai, Zhang, Jia-Min, He, Lin, Bao, Hai-Long, Wu, Xin-Ye.  2019.  Research on a General Fast Analysis Algorithm Model for Pd Acoustic Detection System: The Algorithm Model Design and Its Application. 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA). :22–26.
Nowadays, the detection of acoustical emission is widely used for fault diagnosis of gas insulated substations (GIS) in normal operation and factory tests, which is called 'non-conventional' method recommended in the standard IEC TS 62478-2016 and GIGRE D1.33 444. In this paper, to develop a data analyzer for acoustic detection (AD) system to make an assistant diagnosis for technical personnel or equipment operation and maintenance personnel, based on the previous research on the experimental research, pattern identification with phase compensation and the software development, the algorithm model design and its application is given in detail. For the acoustical emission signals (n, ti, qi), the BP artificial neural network optimized by genetic algorithm (GA-BP) is used as a classifier based on the fingerprint consisting of several statistic operators, which are derivate form typical 2D histograms of PRPD with identification with phase compensation (IPC). Experimental results show that the comprehensive algorithm model designed for identification is practical and effective.
Si, Wen-Rong, Huang, Xing-De, Xin, Zi, Lu, Bing-Bing, Bao, Hai-Long, Xu, Peng, Li, Jun-Hao.  2019.  Research on a General Fast Analysis Algorithm Model for PD Acoustic Detection System: Pattern Identification with Phase Compensation. 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :288–292.
At present, the acoustic emission (AE) method has the advantages of live measurement and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. While the conventional AE detection system or instruments always can't give a right discrimination result, because them always work based on the reference voltage or phase information from an auxiliary 220V voltage signal source rather than the operation high voltage (HV) with the real phase information corresponding to the detected AE pulsed signals. So there is a random phase difference between the reference phase and operation phase. The discharge fingerprint formed by the detected AE pulsed signals with reference phase using the same processing process is compared to the discharge fingerprint database formed in the HV laboratory with the real phase information, therefore, the system may not be able to discriminate the discharge mode of the field measured data from GIS in substation operation. In this paper, in order to design and develop a general fast analysis algorithm model for PD acoustic detection system to make an assistant diagnosis, the pattern identification with phase compensation was designed and applied. The results show that the method is effective and useful to deatl with AE signals meased in operation situation.
2020-07-06
Xu, Zhiheng, Ng, Daniel Jun Xian, Easwaran, Arvind.  2019.  Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1–11.

With the growing scale of Cyber-Physical Systems (CPSs), it is challenging to maintain their stability under all operating conditions. How to reduce the downtime and locate the failures becomes a core issue in system design. In this paper, we employ a hierarchical contract-based resilience framework to guarantee the stability of CPS. In this framework, we use Assume Guarantee (A-G) contracts to monitor the non-functional properties of individual components (e.g., power and latency), and hierarchically compose such contracts to deduce information about faults at the system level. The hierarchical contracts enable rapid fault detection in large-scale CPS. However, due to the vast number of components in CPS, manually designing numerous contracts and the hierarchy becomes challenging. To address this issue, we propose a technique to automatically decompose a root contract into multiple lower-level contracts depending on I/O dependencies between components. We then formulate a multi-objective optimization problem to search the optimal parameters of each lower-level contract. This enables automatic contract refinement taking into consideration the communication overhead between components. Finally, we use a case study from the manufacturing domain to experimentally demonstrate the benefits of the proposed framework.

2020-06-26
Bedoui, Mouna, Bouallegue, Belgacem, Hamdi, Belgacem, Machhout, Mohsen.  2019.  An Efficient Fault Detection Method for Elliptic Curve Scalar Multiplication Montgomery Algorithm. 2019 IEEE International Conference on Design Test of Integrated Micro Nano-Systems (DTS). :1—5.

Elliptical curve cryptography (ECC) is being used more and more in public key cryptosystems. Its main advantage is that, at a given security level, key sizes are much smaller compared to classical asymmetric cryptosystems like RSA. Smaller keys imply less power consumption, less cryptographic computation and require less memory. Besides performance, security is another major problem in embedded devices. Cryptosystems, like ECC, that are considered mathematically secure, are not necessarily considered safe when implemented in practice. An attacker can monitor these interactions in order to mount attacks called fault attacks. A number of countermeasures have been developed to protect Montgomery Scalar Multiplication algorithm against fault attacks. In this work, we proposed an efficient countermeasure premised on duplication scheme and the scrambling technique for Montgomery Scalar Multiplication algorithm against fault attacks. Our approach is simple and easy to hardware implementation. In addition, we perform injection-based error simulations and demonstrate that the error coverage is about 99.996%.

2020-05-22
Ranjan, G S K, Kumar Verma, Amar, Radhika, Sudha.  2019.  K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries. 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). :1—5.
Fault detection in a machine at earlier stage can prevent severe damage and loss to the industries. Fault detection techniques are broadly classified into three categories; signature extraction-based, model-based and knowledge-based approach. Model-based techniques are efficient for raising an alarm signal if there is any fault in the machine. This paper focuses on one such model based-technique to identify the internal faults of induction machine. The model developed is deployed in the end to make it feasible to use in real time. K-Nearest Neighbors (KNN) and grid search cross validation (CV) have been used to train and optimize the model to give the best results. The advantage of proposed algorithm is the accuracy in prediction which has been seen to be 80%. Finally, a user friendly interface has been built using Flask, a python web framework.
2020-05-18
Zhong, Guo-qiang, Wang, Huai-yu, Zheng, Shuai, JIA, Bao-zhu.  2019.  Research on fusion diagnosis method of thermal fault of Marine diesel engine. 2019 Chinese Automation Congress (CAC). :5371–5375.
In order to avoid the situation that the diagnosis model based on single sensor data is easily disturbed by environmental noise and the diagnosis accuracy is low, an intelligent fault fusion diagnosis method for marine diesel engine is proposed. Firstly, the support vector machine which is optimized by genetic algorithm is used to learn the fault sample data from different sensors, then multiple fault diagnosis models and results can be got. After that, multiple groups of diagnosis results are taken as evidence bodies and fused by evidence theory to obtain more accurate diagnosis results. By analyzing the sample data obtained from the fault simulation experiment of marine diesel engine based on AVL BOOST software, the proposed method can improve the fault diagnosis accuracy of marine diesel engine and reduce the uncertainty value of diagnosis results.
Xiaolei, WANG, Zhengning, YU, Xuemin, NIU, Xianfeng, LU, Hao, YANG, Zhongjiawen, LIU.  2019.  Combination Multiple Faults Diagnosis Method Applied to the Aero-engine Based on Improved Signed Directed Graph. 2019 4th International Conference on Measurement, Information and Control (ICMIC). :1–10.
In signed directed graph (SDG) fault diagnosis model, only single fault can be diagnosed. In order to meet the requirements of multiple faults diagnosis, in this paper, improved signed directed graph (ISDG) fault diagnosis model was proposed. The logic and influence between nodes were included in ISDG model. With ISDG model, complex logic can be shown, multiple faults can be diagnosed and the optimal sequence can be determined. Two algorithms are proposed in this paper. One algorithm can obtain the multiple faults combine logic, and the other algorithm can obtain the optimal path of fault diagnosis. According to these two algorithms, the efficiency was improved and the cost was reduced in the multiple fault diagnosis process. Finally, the faults of an aircraft engine bleed system were diagnosed with the interactive algorithm. The proposed algorithms can obtain a diagnosis result effectively. The results of two cases prove that these algorithms can be used for multiple fault diagnosis.
Han, Ying, Li, Kun, Ge, Fawei.  2019.  Multiple Fault Diagnosis for Sucker Rod Pumping Systems Based on Matter Element Analysis with F-statistics. 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). :66–70.
Dynamometer cards can reflect different down-hole working conditions of sucker rod pumping wells. It has great significances to realize multiple fault diagnosis for actual oilfield production. In this paper, the extension theory is used to build a matter-element model to describe the fault diagnosis problem of the sucker rod pumping wells. The correlation function is used to calculate the correlation degree between the diagnostic fault and many standard fault types. The diagnosed sample and many possible fault types are divided into different combinations according to the correlation degree; the F-statistics of each combination is calculated and the “unbiased transformation” is used to find the mean of interval vectors. Larger F-statistics means greater differences within the faults classification; and the minimum F-statistics reflects the real multiple fault types. Case study shows the effectiveness of the proposed method.
Zhao, Xiaohang, Zhang, Ke, Chai, Yi.  2019.  A Multivariate Time Series Classification based Multiple Fault Diagnosis Method for Hydraulic Systems. 2019 Chinese Control Conference (CCC). :6819–6824.
Hydraulic systems is a class of nonlinear complex systems. There are many typical characteristics with the systems: multiple functional components, multiple operation modes, space-time coupling work, and monitoring signals for faults are multivariate time series data, etc. Because of the characteristics, fault diagnosis for Hydraulic systems is not easy. Traditional fault diagnosis methods mostly ignore the multivariable timing characteristics of monitoring signals, it has made many detection and diagnosis (especially for multiple fault) can not keep high accuracy, and some of the methods are not even be able to multiple fault diagnosis. Aim at the problem, a multivariate time series classification based diagnosis method is proposed. Firstly, extracting timing characteristics (transformed features) from the time series data collected via sensors by 1-NN method. Secondly, training the transformed features by multi-class OVO-SVM to classify multivariate time series. Simulation of the method contains single fault and multiple faults conditions, the results show that the method has high accuracy, it can complete multiple-faults classification.
Zhou, Wei, Yang, Weidong, Wang, Yan, Zhang, Hong.  2018.  Generalized Reconstruction-Based Contribution for Multiple Faults Diagnosis with Bayesian Decision. 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). :813–818.
In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method.
Yang, Xiaoliu, Li, Zetao, Zhang, Fabin.  2018.  Simultaneous diagnosis of multiple parametric faults based on differential evolution algorithm. 2018 Chinese Control And Decision Conference (CCDC). :2781–2786.
This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.
Gou, Linfeng, Zhou, Zihan, Liang, Aixia, Wang, Lulu, Liu, Zhidan.  2018.  Dynamic Threshold Design Based on Kalman Filter in Multiple Fault Diagnosis. 2018 37th Chinese Control Conference (CCC). :6105–6109.
The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.
Lal Senanayaka, Jagath Sri, Van Khang, Huynh, Robbersmyr, Kjell G..  2018.  Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks. 2018 XIII International Conference on Electrical Machines (ICEM). :1900–1905.
Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.
Wu, Lan, Su, Sheyan, Wen, Chenglin.  2018.  Multiple Fault Diagnosis Methods Based on Multilevel Multi-Granularity PCA. 2018 International Conference on Control, Automation and Information Sciences (ICCAIS). :566–570.
Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.