Fault Detection and Lubricant Health Monitoring for Slow Bearings in Extreme Operating Conditions

2005 ◽  
Author(s):  
Pattada Kallappa ◽  
Carl Byington ◽  
Bryan Donovan
Keyword(s):  
Fault Detection ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Time To Failure ◽  
Grease Lubrication ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
System Data ◽  
Actuation Systems

Slow rotating bearings are an integral part of aerospace and turbomachinery actuation systems. These actuation systems may be driven by electric, hydraulic or fueldraulic power and often operate under high loads and extreme temperatures. This makes these actuation systems and their slow rotating bearings highly susceptible to degradation and failure. Vibration monitoring techniques are not applicable to the PHM of these bearings, because their slow speeds are unable to produce a measureable vibration signature. Furthermore, the slow bearings are sealed and use grease lubrication, thus eliminating traditional oil debris monitoring. To address these problems, Impact Technologies, LLC has developed a PHM system that relies on system identification and uses available control system data and sensor measurements. This PHM system consists of algorithms and models that perform fault detection and identification for the bearings and its actuation train components like valves, pumps, motors, gears and bearings. The PHM process is divided into two stages — diagnostics and prognostics. Diagnostics is the process of detecting and isolating faults, while prognostics is the process of predicting remaining useful life (RUL) or time to failure. The authors demonstrate the PHM system through simulation on a dynamic model that is representative of hydraulic-mechanical actuation systems used in new and existing manned aircrafts, UAVs and Short Take-off and Vertical Landing aircrafts.

scholarly journals PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mohamed Ali Zdiri ◽  
Mohsen Ben Ammar ◽  
Fatma Ben Salem ◽  
Hsan Hadj Abdallah
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Diagnostic Method ◽  
Operating Conditions ◽  
Drive System ◽  
Diagnostic Methods ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Fault Detection And Identification ◽  
Fuzzy Logic Technique

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

Performance investigation of an advanced diagnostic method for SSTPI-fed IM drives under single and multiple open IGBT faults

2019 ◽  
Vol 38 (2) ◽  
pp. 616-641 ◽  
Author(s):  
Mohamed Ali Zdiri ◽  
Badii Bouzidi ◽  
Hsan Hadj Abdallah
Keyword(s):  
Fault Detection ◽  
Diagnostic Method ◽  
Open Circuit ◽  
Operating Conditions ◽  
False Alarms ◽  
Content Type ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Fault Detection And Identification ◽  
New Diagnosis

Purpose This paper aims to analyze and investigate the performance of an improved fault detection and identification (FDI) method based on multiple criteria, applied to six-switch three-phase inverter (SSTPI)-fed induction motor (IM) drives under both single and multiple open insulated-gate bipolar transistors(IGBT) faults. Design/methodology/approach This paper proposes an advanced diagnostic method for both single and multiple open IGBT faults dedicated to SSTPI-fed IM drives considering five distinct faulty operating conditions as follows: a single IGBT open-circuit fault, a single-phase open-circuit fault, a non-crossed double fault in two different legs, a crossed double fault in two different legs and a three-IGBT open-circuit fault. This is achieved because of the introduction of a new diagnosis variable provided using the information of the slope of the current vector in (α-β) frame. The proposed FDI method is based on the synthesis and the analysis, under both healthy and faulty operations, of the behaviors of the introduced diagnosis variable, the three motor phase currents and their normalized average values. Doing so, the developed FDI method allows a best compromise of fast detection and precision localization of IGBT open-circuit fault of the inverter. Findings Simulation works, carried out considering the implementation of the direct rotor flux oriented control in an IM fed by the conventional SSTPI, have proved the high performance of the advanced FDI method in terms of fast fault detection associated with a high robustness against false alarms, against speed and load torque fast variations and against the oscillations of the DC-bus voltage in the case of both healthy and faulty operations. Research limitations/implications This work should be extended considering the validation of the obtained simulation results through experiments. Originality/value Different from other FDI methods, which suffer from a low diagnostic effectiveness for low load levels and false alarms during transient operation, this method offers the potentialities to overcome these drawbacks because of the introduction of the new diagnosis variable. This latter, combined with the information provided from the three motor phase currents and their normalized average values allow a more efficient detection and identification of IGBT open-circuit fault.

scholarly journals A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2922
Author(s):  
Fan Zhang ◽  
Ye Wang ◽  
Yanbin Gao
Keyword(s):  
Fault Detection ◽  
Parametric Bootstrap ◽  
Integrated System ◽  
Range Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

scholarly journals Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

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