Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To o...Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors.展开更多
A software fault injection system SFIS is designed,which consists of the target system plus a fault injector,fault library,workload,data collector,and data analyzer. A serial communication mechanism is adopted to simu...A software fault injection system SFIS is designed,which consists of the target system plus a fault injector,fault library,workload,data collector,and data analyzer. A serial communication mechanism is adopted to simulate the factual work environment. Then a fault model is built for single particle event,which can be denoted as FM=(FL,FT). FL stands for fault location,and FT stands for fault type. The fault model supports three temporal faults: transient,intermittent,and permanent. During the experiments implemented by SFIS,the software interruption method is adopted to inject transient faults,and step trace method is adopted to inject permanent faults into the target system. The experiment results indicate that for the injected transient code segment faults,2.8 % of them do not affect the program output,80.1% of them are detected by the built-in error detection in the system,and 17.1% of them are not detected by fault detection mechanism. The experiment results verify the validity of the fault injection method.展开更多
文摘Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors.
基金National Defense Scientific Work Committee Foundation of China (Grant No.16.6.2.7).
文摘A software fault injection system SFIS is designed,which consists of the target system plus a fault injector,fault library,workload,data collector,and data analyzer. A serial communication mechanism is adopted to simulate the factual work environment. Then a fault model is built for single particle event,which can be denoted as FM=(FL,FT). FL stands for fault location,and FT stands for fault type. The fault model supports three temporal faults: transient,intermittent,and permanent. During the experiments implemented by SFIS,the software interruption method is adopted to inject transient faults,and step trace method is adopted to inject permanent faults into the target system. The experiment results indicate that for the injected transient code segment faults,2.8 % of them do not affect the program output,80.1% of them are detected by the built-in error detection in the system,and 17.1% of them are not detected by fault detection mechanism. The experiment results verify the validity of the fault injection method.
