Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Noshad, Zainib; Javaid, Nadeem; Saba, Tanzila; Wadud, Zahid; Saleem, Muhammad Qaiser; Alzahrani, Mohammad Eid; Sheta, Osama E.

doi:10.3390/s19071568

Cited by 114 publications

(61 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To evaluate the performance of the CNN-RF method, other traditional methods were selected for a comparison of prediction accuracy. The selected methods were KNN [11], ELM [15], SVM [17], LVQ [18], [19], BP [20], RF [39], and CNN-RF, the last of which (i.e., the proposed model) had higher accuracy than the other methods under noisy environment. The comparison results, in terms of sensor fault diagnosis accuracy, are shown in Table 5.…”

Section: ) Cnn-rf Inferencementioning

confidence: 99%

See 1 more Smart Citation

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis. INDEX TERMS Fault diagnosis, hydrogen sensor, convolutional neural network, random forest, feature extraction.

show abstract

Section: ) Cnn-rf Inferencementioning

confidence: 99%

“…Therefore, it is more suitable to use RF when respect to a large number of data with reasonable features, especially under noisy environment. The literature [38], [39] has demonstrated that sensors fault diagnosis based on RF is feasible.…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…RF is intended to achieve an optimized, supervised and structured resolution for labelled problems, which is proven to given more accurate results compared to many other supervised machine learning algorithms. In [9], RF is compared to numerous classifiers of different functionalities for its ability to overcome two occurring sensor faults in Wireless Sensor Networks (WSNs), which are spike fault and data loss fault. This study represents an elaborated comparison between RF, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Probabilistic Neural Network (PNN), using Detection Accuracy (DA), Matthews Correlation Coefficients (MCC), True Positive Rate (TPR) and F1-score as the comparison criteria that determine the overall rank of each method.…”

Section: Related Work: Rf For Fdd In Industry 40mentioning

confidence: 99%

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Mallak¹,

Fathi²

2020

Sci

View full text Add to dashboard Cite

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

show abstract

“…Fault detection and diagnosis can improve the reliability of sensor networks and enhance its bandwidth of operation. There are various techniques as reported in recent research surveys related to fault detection and diagnosis in sensor networks [10][11][12][13]. Incidentally, it is worth noting that while choosing fault detection methods, care should be taken to reduce the energy consumption and ease of integration with the existing networks.…”

Section: Introductionmentioning

confidence: 99%

“…If MCC is of +0.7 or higher, it can be considered as excellent, from +0.4 to +0.69, it can categorized as good and from +0.30 to +0.39 it is moderate. When it reaches +0.20 to +0.29, it is considered as weak.The F1-Score[13] is used to measure the performance of the classifier based on false positive and false negative values. It is defined as follows.…”

mentioning

confidence: 99%

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Emperuman

Chandrasekaran

2020

Sensors

View full text Add to dashboard Cite

Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

show abstract

Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Cited by 114 publications

References 34 publications

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Contact Info

Product

Resources

About