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Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.

Lee W, Park CG - Sensors (Basel) (2014)

Bottom Line: The performance of fault detection and isolation is influenced by the relative size of noise and fault.The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT.To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, ASRI, Seoul National University, Seoul 151-741, Korea. clever212@snu.ac.kr.

ABSTRACT
A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).

No MeSH data available.


Fault detection using EPSA.
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f2-sensors-14-03428: Fault detection using EPSA.

Mentions: The fault detection concept of EPSA is basically the same as that of the PSA. However, with PSA it is only possible to find single faults. EPSA is designed to solve the single and double faults detection problem. To detect the double faults, seven sensor groups that are composed of six sensors each are used. Each sensor group is used to determine whether the fault occurred or not. The fault detection number of the sensor group is used to decide which type of fault happened. Figure 2 shows the EPSA process for fault detection. To ascertain whether each sensor group has the fault, we check the size of the parity vector using Equation (4). The Fault Detection Number (FDN) is calculated by adding the number of faulty groups. As a result, this FDN value refers to the type of fault that occurred. If the FDN is 6, it means a single fault occurred. If FDN is 2 and 7, it means type C and B double faults occurred, respectively. After the fault detection, a fault isolation process is performed according to the fault type as indicated in Figure 3.


Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.

Lee W, Park CG - Sensors (Basel) (2014)

Fault detection using EPSA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958265&req=5

f2-sensors-14-03428: Fault detection using EPSA.
Mentions: The fault detection concept of EPSA is basically the same as that of the PSA. However, with PSA it is only possible to find single faults. EPSA is designed to solve the single and double faults detection problem. To detect the double faults, seven sensor groups that are composed of six sensors each are used. Each sensor group is used to determine whether the fault occurred or not. The fault detection number of the sensor group is used to decide which type of fault happened. Figure 2 shows the EPSA process for fault detection. To ascertain whether each sensor group has the fault, we check the size of the parity vector using Equation (4). The Fault Detection Number (FDN) is calculated by adding the number of faulty groups. As a result, this FDN value refers to the type of fault that occurred. If the FDN is 6, it means a single fault occurred. If FDN is 2 and 7, it means type C and B double faults occurred, respectively. After the fault detection, a fault isolation process is performed according to the fault type as indicated in Figure 3.

Bottom Line: The performance of fault detection and isolation is influenced by the relative size of noise and fault.The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT.To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, ASRI, Seoul National University, Seoul 151-741, Korea. clever212@snu.ac.kr.

ABSTRACT
A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).

No MeSH data available.