scholarly journals Model Based IAS Analysis for Fault Detection and Diagnosis of IC Engine Powertrains

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 565
Author(s):  
Yuandong Xu ◽  
Baoshan Huang ◽  
Yuliang Yun ◽  
Robert Cattley ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
High Efficiency ◽  
Experimental Studies ◽  
Cost Effective ◽  
Angular Speed ◽  
Fault Detection And Diagnosis ◽  
Large Engine ◽  
Ic Engine ◽  
Model Based ◽  
Cost Effective Approach ◽  
Detection And Diagnosis

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

Model-based fault detection and diagnosis for hydraulic braking systems

2003 ◽  
Vol 36 (5) ◽  
pp. 307-312 ◽  
Author(s):  
Harald Straky ◽  
Marco Muenchhof ◽  
Rolf Isermann
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Model Based ◽  
Detection And Diagnosis

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

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

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, spotting 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 3-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. 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, in order 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, as well as 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.

scholarly journals Fault Diagnosis in a Centrifugal Pump Using Active Magnetic Bearings

2004 ◽  
Vol 10 (3) ◽  
pp. 183-191 ◽  
Author(s):  
Rainer Nordmann ◽  
Martin Aenis
Keyword(s):  
Fault Detection ◽  
Centrifugal Pump ◽  
Fault Detection And Diagnosis ◽  
Magnetic Bearings ◽  
Diagnostic Tools ◽  
Contactless Measurement ◽  
Active Magnetic Bearings ◽  
Model Based ◽  
Detection And Diagnosis ◽  
Conventional Systems

The number of rotors running in active magnetic bearings (AMBs) has increased over the last few years. These systems offer a great variety of advantages compared to conventional systems. The aim of this article is to use the AMBs together with a developed built-in software for identification, fault detection, and diagnosis in a centrifugal pump. A single-stage pump representing the turbomachines is investigated. During full operation of the pump, the AMBs are used as actuators to generate defined motions respectively forces as well as very precise sensor elements for the contactless measurement of the responding displacements and forces. In the linear case, meaning small motions around an operating point, it is possible to derive compliance frequency response functions from the acquired data. Based on these functions, a model-based fault detection and diagnosis is developed which facilitates the detection of faults compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the model-based diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.

Model-based On-Board Fault Detection and Diagnosis for Automotive Engines

1991 ◽  
Vol 24 (6) ◽  
pp. 503-508 ◽  
Author(s):  
J.J. Gertler ◽  
M. Costin ◽  
Xiaowen Fang ◽  
R. Hira ◽  
Z. Kowalczuk ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Automotive Engines ◽  
Model Based ◽  
Detection And Diagnosis
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