CONDITION MONITORING OF ROLLER BEARING USING ENHANCED DEMPSTER/SHAFER EVIDENCE THEORY

According to the generalized Jaccard coefficient and false degree, an improved approach is proposed by incorporating Dempster-Shafer proofs for determining the level of confidence in the evidence. It also determines the weight of proof in terms of trust and falsity. Then, the base probability of the original evidence is weighted and averaged, followed by the adoption of the combined Dempster's compositional rule. It is evident that the above combination can be applied in condition monitoring of bearings up to rupture. Firstly, the supporting vibration signal is decomposed by applying the empirical mode decomposition, empirical wavelet transformation and variational mode decomposition approaches. All the vectors of the fault characteristic are extracted by combining the sample entropy. Then, the fault probability is obtained by performing preliminary diagnosis using the relevance vector machine, where the obtained preliminary diagnostic result is considered as the primary probability of the Dempster-Shafer evidence theory. Finally, it is revealed that an accurate diagnosis could be achieved by performing fusion using the enhanced evidence combination method. Specifically, the accuracies of the initial condition monitoring based on the EMD, EWT and VMD sample entropies and RVM were found to be 97.5%, 98.75% and 95%, respectively. The closeness and high values of these accuracies show that the selected methods are valid. The obtained condition monitoring results show that the relevance vector machine combined with the Dempster-Shafer evidence could enhance the efficiency. This theory has the least error and better reliability in supporting failure diagnosis

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

A fault diagnosis model based on singular value manifold features, optimized SVMs and multi-sensor information fusion

To achieve better fault diagnosis of rotating machinery, this paper presents a novel intelligent fault diagnosis model based on singular value manifold features (SVMF), optimized support vector machines (SVMs) and multi-sensor information fusion. Firstly, a new fault feature named SVMF is developed to better represent faults. SVMF is acquired by extracting manifold topology features of the singular spectrum. Compared with frequently-used fault features, the feature scale of SVMF is constant for variable rotating speed, and the extraction process of SVMF also has the effect of self-weighting. So SVMF has a better representation of faults. Then, to select optimal parameters for model training of SVMs, an improved fruit fly algorithm is proposed by introducing a guidance search mechanism and enhanced local search operation, and as a result both the convergence speed and accuracy are improved. Finally, the Dempster–Shafer evidence theory is introduced to fuse decision-level information from SVM models of multiple sensors. Information fusion eliminates the conflict of conclusions on fault diagnosis from multiple sensors, which leads to high robustness and accuracy of the fault diagnosis model. As a summary, the proposed method combines the advantages of SVMF in fault representation, SVMs in fault identification and the Dempster–Shafer evidence theory in information fusion, and as a result the proposed method will perform better at fault diagnosis. The proposed intelligent fault diagnosis model is subsequently applied to fault diagnosis of the gearbox. Experimental results show that the proposed diagnostic framework is versatile at detecting faults accurately

CONFIDENCE-BASED DECISION-MAKING SUPPORT FOR MULTI-SENSOR SYSTEMS

We live in a world where computer systems are omnipresent and are connected to more and more sensors. Ranging from small individual electronic assistants like smartphones to complex autonomous robots, from personal wearable health devices to professional eHealth frameworks, all these systems use the sensors’ data in order to make appropriate decisions according to the context they measure. However, in addition to complete failures leading to the lack of data delivery, these sensors can also send bad data due to influences from the environment which can sometimes be hard to detect by the computer system when checking each sensor individually. The computer system should be able to use its set of sensors as a whole in order to mitigate the influence of malfunctioning sensors, to overcome the absence of data coming from broken sensors, and to handle possible conflicting information coming from several sensors. In this thesis, we propose a computational model based on a two layer software architecture to overcome this challenge. In a first layer, classification algorithms will check for malfunctioning sensors and attribute a confidence value to each sensor. In the second layer, a rule-based proactive engine will then build a representation of the context of the system and use it along some empirical knowledge about the weaknesses of the different sensors to further tweak this confidence value. Furthermore, the system will then check for conflicting data between sensors. This can be done by having several sensors that measure the same parameters or by having multiple sensors that can be used together to calculate an estimation of a parameter given by another sensor. A confidence value will be calculated for this estimation as well, based on the confidence values of the related sensors. The successive design refinement steps of our model are shown over the course of three experiments. The first two experiments, located in the eHealth domain, have been used to better identify the challenges of such multi-sensor systems, while the third experiment, which consists of a virtual robot simulation, acts as a proof of concept for the semi-generic model proposed in this thesis

Effective Fault Diagnosis in Chemical Plants By Integrating Multiple Methodologies

Ph.DDOCTOR OF PHILOSOPH