Fault Diagnosis Method for Industrial Robots based on Dimension Reduction and Random Forest

2021 ◽  
Author(s):  
Zi Fang ◽  
Linhui Zhou ◽  
Zeyu Fu ◽  
Zhuang Fu ◽  
Yisheng Guan
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Dimension Reduction ◽  
Industrial Robots ◽  
Diagnosis Method

scholarly journals Fault diagnosis method of submersible screw pump based on random forest

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242458
Author(s):  
Minzheng Jiang ◽  
Tiancai Cheng ◽  
Kangxing Dong ◽  
Shufan Xu ◽  
Yulong Geng
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Decision Trees ◽  
Processing System ◽  
Random Forest Model ◽  
Categorical Variables ◽  
Oil Well ◽  
Screw Pump ◽  
Forest Model ◽  
Diagnosis Method

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

Fault Diagnosis Method for Inter-Shaft Bearings Based on Information Exergy and Random Forest

2018 ◽  
Author(s):  
Jing Tian ◽  
Yanting Ai ◽  
Ming Zhao ◽  
Chengwei Fei ◽  
Fengling Zhang
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Random Forest ◽  
Complex Signals ◽  
Bearing Faults ◽  
Outer Race ◽  
Bearing Fault ◽  
Wavelet Energy ◽  
Diagnosis Method ◽  
Ae Signals

To reasonably process the complex signals and improve the diagnosis accuracy of inter-shaft bearing incipient faults, this paper develops wavelet energy spectrum exergy (WESE) and random forest (RF) (short for WESE-RF) method with respect to acoustic emission (AE) signals. Inter-shaft bearing faults, which contain inner race fault, outer race fault, rolling element faults and normal status under different measuring points and different rotational speeds, are simulated based on the test rig of inter-shaft bearings, to collect the AE signals of these faults. Regarding the AE signals of inter-shaft bearing faults, the WESE values, one signal feature, are extracted from an information exergy perspective, and are applied to structure feature vectors. The WESE values of these AE signals are regarded as the sample set which include the training samples subset used to establish the WESE-RF model of fault diagnosis and the test samples subset applied to test the effectiveness of the developed WESE-RF model. The investigation on the fault diagnosis of inter-shaft bearing demonstrates the fault diagnosis method with the WESE-RF has good generalization ability and high diagnostic accuracy of over 0.9 for inter-shaft bearing fault. The efforts of this paper provide a useful approach-based information exergy and wavelet energy spectrum for inter-shaft bearing fault diagnosis.

scholarly journals Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 470
Author(s):  
Zijian Guo ◽  
Mingliang Liu ◽  
Huabin Qin ◽  
Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Recognition Rate ◽  
Rotating Machinery ◽  
Bootstrap Sample ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.

scholarly journals Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 57 ◽  
Author(s):  
Jing Tian ◽  
Lili Liu ◽  
Fengling Zhang ◽  
Yanting Ai ◽  
Rui Wang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Random Forest ◽  
High Sensitivity ◽  
Generalization Ability ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Ae Signal ◽  
Ae Signals

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

scholarly journals Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1088 ◽  
Author(s):  
Gaowei Xu ◽  
Min Liu ◽  
Zhuofu Jiang ◽  
Dirk Söffker ◽  
Weiming Shen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Multi Level

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

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