Online Health Monitoring Research Articles

Impact of Duty Cycle on Wear Progression in Variable-displacement Vane Oil Pumps

Variable-displacement vane oil pumps are increasingly employed in automotive powertrains for their efficiency benefits through reduced losses. However, confirming long life reliability of a new commodity based on limited data available from product development tests and early field experience is a significant challenge, which is addressed by the work presented in this paper. The approach presented combines physical examination of pumps returned from tests, with analysis of damage factors for pump wear progression, and an analysis of functional parameters for the powertrain system focused on the cause effect linkages across the systems hierarchy. The metrology results from physical measurements of used parts provide useful insights for the wear progression and the expected service performance of the pump. The paper also expands towards a data driven approach based on ECU data analysis that could provide a pathway towards the development of online health monitoring and diagnostics of the oil pumps.

HCViewer: software and technology for quality control and processing raw mass data of preventive screening

AbstractA prototype automated system for the analysis and processing raw data of mass population screening in the Russian health centers (HCs), software program HCViewer, is developed. In it, essential elements of big data, including data filtering (i.e., detection and removal of outliers and fraud cases), statistics and spatial visualization are implemented. Testing HCViewer on the datasets of knowingly reliable, unreliable, and partially reliable bioimpedance body composition data has shown high diagnostic specificity (95.2–98.9%) and sensitivity (94.5–99.2%) of the filtering criteria. Retrospective analysis of the HCs’ bioimpedance database revealed rapid growth in the proportion of fraud cases, from 5–7% in 2009 to 43–45% in 2013–2014 (with the correction for false alarm rate), and the high level of heterogeneity in data quality, so that 80.3% of the incorrect data were generated in 20% of the HCs. This indicates inefficient management of the Russian preventive screening system and suggests incomparability and, therefore, uselessness of the related health statistics from some regions without a preliminary filtering of the underlying raw data. Our results show the potential utility of HCViewer for the dynamical quality control of the preventive screening data, as well as for the online health monitoring and modelling potential effects of various control measures aimed at the prevention of non-communicable diseases. After application of the selection criteria, a cross-sectional 2009–2015 database of bioimpedance measurements of 1.27 million individuals aged 5–96 years was formed for the assessment of population health. On the whole, fraud prevention should become the responsibility of the Russian health care system to ensure an efficiency of its function.

Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters

In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring; afterward, effective estimations of a possible structural damage are provided online by tracking the evolution in time of stiffness parameters and projection bases handled in the model order reduction procedure. Such tracking is accomplished via two Kalman filters: a first (extended) one to deal with the time evolution of a joint state vector, gathering the reduced-order state and the stiffness terms degraded by damage; a second one to deal with the update of the reduced-order model in case of damage evolution. Both filters exploit the information conveyed by measurements of the structural response to the external excitations. Results are reported for a (pseudo-experimental) benchmark test on an eight-story shear building. Capability and performance of the proposed approach are assessed in terms of tracked variation of the stiffness terms of the reduced-order model, identified damage location and speed-up of the whole health monitoring procedure.

Parametric diagnosis of the adaptive gas path in the automatic control system of the aircraft engine

The paper dwells on the adaptive multimode mathematical model of the gas-turbine aircraft engine (GTE) embedded in the automatic control system (ACS). The mathematical model is based on the throttle performances, and is characterized by high accuracy of engine parameters identification in stationary and dynamic modes. The proposed on-board engine model is the state space linearized low-level simulation. The engine health is identified by the influence of the coefficient matrix. The influence coefficient is determined by the GTE high-level mathematical model based on measurements of gas-dynamic parameters. In the automatic control algorithm, the sum of squares of the deviation between the parameters of the mathematical model and real GTE is minimized. The proposed mathematical model is effectively used for gas path defects detecting in on-line GTE health monitoring. The accuracy of the on-board mathematical model embedded in ACS determines the quality of adaptive control and reliability of the engine. To improve the accuracy of identification solutions and sustainability provision, the numerical method of Monte Carlo was used. The parametric diagnostic algorithm based on the LPτ - sequence was developed and tested. Analysis of the results suggests that the application of the developed algorithms allows achieving higher identification accuracy and reliability than similar models used in practice.

Progressive damage identification using dual extended Kalman filter

Existing Kalman filter-based parameter identification algorithms estimate the system parameters as either sole states or a subset of augmented states. While the former approach requires the measurement to be sufficiently clean, the latter is reported to have numerical stability issues. Since the parameters are estimated in both these approaches in an optimal sense, in the presence of a significant variation in parameters (due to damage), the estimates may often diverge. In this article, we propose an online health monitoring scheme powered by dual extended Kalman filtering technique to simultaneously estimate the system parameters along with the response states of a reduced-order system. To capacitate damage localization beyond sensor resolution, the proposed method employs location-based structural properties as system parameter. This reduces the dimensionality of the formulation from \({4n^2}\) elements in the state matrix to only a few physical parameters. Unnecessary estimation of a large number of unmeasured response states has been avoided by employing the system reduction technique and thus by describing the system using only measured DOFs. This in turn enables estimating a poorly observed system as a fully observed one. Two numerical experiments are performed on two degrading structures: an Euler–Bernoulli beam and a bridge truss to demonstrate the competency of the algorithm with reduced-order models.

Precursors to self-sustained oscillations in aeroacoustic systems

In this study, we attempt to provide a repertoire of measures to forewarn the onset of impending flow-induced mechanical oscillations via online health monitoring. To illustrate the principles, the flow of air through a pipe terminated by a circular orifice plate is investigated at various flow velocities using a suitably placed pressure transducer. It is observed that the regimes corresponding to the production of a tone is presaged by operating conditions that display temporarily intermittent bursts of periodic pressure oscillations that emerge from a background of lower-amplitude aperiodic fluctuations. The various model-free measures prescribed in this paper serve as efficient precursors by characterizing these intermittent states, which can potentially arise in aeroacoustic systems when the flow is highly unsteady or turbulent.

Probe Improvement of Inductive Sensor for Online Health Monitoring of Mechanical Transmission Systems

Metal debris in lubricating oil contains abundant information regarding the ageing and wear/damage of mechanical transmission systems. The health condition of the lubricated systems can be indicated by the concentration and size of the metal abrasive particles, which may provide very early warnings of faults/failures and benefit the condition-based maintenance. An improved inductive sensor that uses saddle-coil probe to generate a uniform magnetic field for performance improvement on the metal particle detection is proposed in this paper. The detailed geometry and the performance analysis are presented by using finite element analysis. The results demonstrate that the proposed saddle-coil inductive sensor possesses good identification ability of recognizing the ferromagnetic and nonferromagnetic oil debris particles whose diameters are as small as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100~\mu \text{m}$ </tex-math></inline-formula> .

A new magnetic flux leakage sensor based on open magnetizing method and its on-line automated structural health monitoring methodology

Magnetic flux leakage sensors have been widely used for the structural health monitoring of ferrous materials (structural health monitoring). This article provides a new magnetic flux leakage sensor based on open magnetizing method. Starting with the traditional yoke magnetization method analyses, the provided open magnetizing method and testing principle are presented. Then, the comparative analyses between the two methods are conducted by finite element method, indicating that the open magnetizing method features less magnetic interaction force, more uniform magnetic flux density for swing scanning, simpler architectures, and more universal functions under the framework of similar magnetic excitation capability for defects. Furthermore, the sensor based on open magnetizing method is designed through optimization and the advantages for open magnetization method are confirmed by experiments with created sensors. Finally, on-line structural health monitoring methodology for the monitoring probe with “rigid” open–close and “free” tracking rope-swing functions is developed based on the proposed sensors. Meanwhile, experimental comparisons between the open and yoke probes for on-line automated monitoring are also conducted to successfully confirm the characters of smaller magnetic interaction force, less wear, and damage in contrast to the traditional on-line automated structural health monitoring technology. Additionally, the on-line automated magnetic flux leakage apparatus for the structural health monitoring of hoist wire ropes is developed and applied, which further confirms the good practicability for the structural health monitoring of hoist ropes under the poor working conditions.

Finite element model validation of bridge based on structural health monitoring—Part I: Response surface-based finite element model updating

In the engineering practice, merging statistical analysis into structural evaluation and assessment is a tendency in the future. As a combination of mathematical and statistical techniques, response surface (RS) methodology has been successfully applied to design optimization, response prediction and model validation. With the aid of RS methodology, these two serial papers present a finite element (FE) model updating and validation method for bridge structures based on structural health monitoring. The key issues to implement such a model updating are discussed in this paper, such as design of experiment, parameter screening, construction of high-order polynomial response surface model, optimization methods and precision inspection of RS model. The proposed procedure is illustrated by a prestressed concrete continuous rigid-frame bridge monitored under operational conditions. The results from the updated FE model have been compared with those obtained from online health monitoring system. The real application to a full-size bridge has demonstrated that the FE model updating process is efficient and convenient. The updated FE model can relatively reflect the actual condition of Xiabaishi Bridge in the design space of parameters and can be further applied to FE model validation and damage identification.

Damage detection with streamlined structural health monitoring data.

The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.

An integrated lubricant oil conditioning sensor using signal multiplexing

One effective approach to detect signs of potential failure of a rotating or reciprocating machine is to examine the conditions of its lubrication oil. Here we present an integrated oil condition sensor for detecting both wear debris and lubricant properties. The integrated sensor consists of miniature multiplexed sensing elements for detection of wear debris and measurements of viscosity and moisture. The oil debris sensing element consists of eight sensing channels to detect wear debris in parallel; the elements for measuring oil viscosity and moisture, based on interdigital electrode sensing, were fabricated using micromachining. The integrated sensor was installed and tested in a laboratory lubricating system. Signal multiplexing was applied to the outputs of the three sensing elements such that responses from all sensing elements were obtained within two measurements, and the signal-to-noise ratio was improved. Testing results show that the integrated sensor is capable of measuring wear debris (>50 µm), moisture (>50 ppm) and viscosity (>12.4 cSt) at a high throughput (200 ml min−1). The device can be potentially used for online health monitoring of rotating machines.

A New Method for Monitoring Gears Surface Failures Using Enhanced Image Registration Approach

In this paper, we present an image registration approach to cope with inter-image illumination changes of arbitrary shape in order to monitor the development of micro-pitting in transmission gears. Traditional image registration approaches do not typically account for inter-image illumination variations that negatively affect the geometric registration precision. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, the correlation between the resulting aligned images is compared to a reference one. The presented image registration approach is used with an online health monitoring system involving the analysis of vibration, acoustic emission and oil debris to follow the development of micro-pitting in transmission gears. The proposed monitoring system achieves more registration precision compared to competing systems. This paper experimentally validates the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems and overcome the disadvantages of the most commonly used methods, such as gear flank profile scanning, replica sample analysis and conventional image analysis.

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.

Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods

In this paper, we investigate the use of first-order reliability methods to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in engineering applications. The prediction of RUL is affected by several sources of uncertainty, and it is important to systematically quantify their combined effect on the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical approaches are computationally cheaper, and sometimes they are better suited for online decision-making. Exact analytical algorithms may not be available for practical engineering applications, but effective approximations can be made using first-order reliability methods. This paper describes three first-order reliability-based methods for RUL uncertainty quantification: first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (inverse-FORM). The inverse-FORM methodology is particularly useful in the context of online health monitoring, and this method is illustrated using the power system of an unmanned aerial vehicle, where the goal is to predict the end of discharge of a lithium-ion battery.

Real-Time Temperature Estimation for Power MOSFETs Considering Thermal Aging Effects

This paper presents a novel real-time power-device temperature estimation method that monitors the power MOSFET's junction temperature shift arising from thermal aging effects and incorporates the updated electrothermal models of power modules into digital controllers. Currently, the real-time estimator is emerging as an important tool for active control of device junction temperature as well as online health monitoring for power electronic systems, but its thermal model fails to address the device's ongoing degradation. Because of a mismatch of coefficients of thermal expansion between layers of power devices, repetitive thermal cycling will cause cracks, voids, and even delamination within the device components, particularly in the solder and thermal grease layers. Consequently, the thermal resistance of power devices will increase, making it possible to use thermal resistance (and junction temperature) as key indicators for condition monitoring and control purposes. In this paper, the predicted device temperature via threshold voltage measurements is compared with the real-time estimated ones, and the difference is attributed to the aging of the device. The thermal models in digital controllers are frequently updated to correct the shift caused by thermal aging effects. Experimental results on three power MOSFETs confirm that the proposed methodologies are effective to incorporate the thermal aging effects in the power-device temperature estimator with good accuracy. The developed adaptive technologies can be applied to other power devices such as IGBTs and SiC MOSFETs, and have significant economic implications.

Online health monitoring of a fuel cell using total harmonic distortion analysis

The present work aims to show the nonlinear behavior of a PEM fuel cell under different mass transport conditions. Understanding this behavior helps in online state-of-health monitoring and control of a fuel cell stack. To analyze the health of an operating stack, a total harmonic distortion analysis (THDA) system requires only the sum of voltages or currents of the stack to be monitored. A low-frequency current or voltage signal is impressed on the fuel cell stack, and the resulting voltage or current signal is measured. To determine any change in harmonics, the measured signal is processed with a harmonic analyzer. The operational states of individual cells of the fuel cell stack may be inferred from at least one change in the harmonic content of the impressed signal. The mass transport problem related to the cathode and anode is distinguished using mixed-frequency signals. The present study found experimentally that hydrogen starvation is dominantly observed in the harmonic analysis only below a frequency of 15 Hz, whereas air starvation showed harmonic changes at frequencies below 100 Hz. Total harmonic distortions were observed to rise to 2–2.5% under both the starvation conditions but with different frequency signals.

Experimental study on engine gas-path component fault monitoring using exhaust gas electrostatic signal

This paper presents the recent development in engine gas-path components health monitoring using electrostatic sensors in combination with signal-processing techniques. Two ground-based engine electrostatic monitoring experiments are reported and the exhaust gas electrostatic monitoring signal-based fault-detection method is proposed. It is found that the water washing, oil leakage and combustor linear cracking result in an increase in the activity level of the electrostatic monitoring signal, which can be detected by the electrostatic monitoring system. For on-line health monitoring of the gas-path components, a baseline model-based fault-detection method is proposed and the multivariate state estimation technique is used to establish the baseline model for the electrostatic monitoring signal. The method is applied to a data set from a turbo-shaft engine electrostatic monitoring experiment. The results of the case study show that the system with the developed method is capable of detecting the gas-path component fault in an on-line fashion.

Lubricants Health Monitoring

&#x0D; &#x0D; &#x0D; Fault inception and growth in the components of a mechanical power drive are often heavily affected by the lubricant health. As a consequence, monitoring the lubricant health status and signaling a degradation of the lubricant properties could improve the reliability of the mechanical drive. A further merit of implementing a lubricant health monitoring system is the possibility to move from a time- based maintenance to a condition-based maintenance with the ensuing decrease of operating costs and reduction of the environmental impact. This would be of a particular advantage for those applications in which loss of operation generated by poor lubrication could cause a service disruption or lead to a safety critical condition. The proposed paper presents the initial part of an ongoing research activity on lubricants health monitoring. The work so far performed consisted of the following activities: perform a critical review of the studies addressing the lubricants condition; determine which are the significant features of the lubricants used in the majority of mechanical systems, that need to be extracted to assess the lubricant health; which occurences affect the lubricant health and how the oil health status can be assessed by different techniques, whose relative merits will be discussed; define the progression of the degradation of the lubricant health status; present the initial study of how different lubricant health monitoring techniques can be fused together to develop an efficient on-line lubricant health monitoring system. The paper thus presents the summary of the work performed to establish the fundamentals onto which a reliable PHM system for the lubricants of mechanical drives can be developed, that will be the final objective of the on-going research activity.&#x0D; &#x0D; &#x0D;

Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts

Delaminations are one of the most prevalent defects noticed in laminated composite structures. The existence of delaminations changes the vibration characteristics of laminates and hence such indicators can be used to quantify the health characteristics of laminates and detect potential risk of catastrophic failures. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using both numerical models and experimental studies of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used for the solution of the inverse problem to determine the interface, size and location of delaminations using changes in measured natural frequencies before and after damage. The algorithms successfully performed delamination detection given limited amount of training datasets. Since in all practical problems, noise and error are inherently present, the performance of the proposed approach is also evaluated under varying levels of noise and error. The results clearly highlight the efficiency and the robustness of the approach. Hence, a delamination prediction strategy via K-means clustering, ANN and optimization algorithms integrated with surrogate models based on ANN for computational enhancement have been successfully developed and found efficient for detection of the interface of delamination, its size and location in FRP composite laminates using variations in natural frequencies. The developed algorithms are applied to composite beams. Results clearly indicate the delamination detection capability of the algorithms. The results demonstrate that the proposed techniques provide remarkable accurate detection of delamination damage with error and noise corrupted natural frequency validation data. The algorithms are quite promising, successful, flexible and practical with significant increase in terms of efficiency and effectiveness for SHM.

Neural network approach for damaged area location prediction of a composite plate using electromechanical impedance technique

Abstract Nowadays, breakthrough composite technologies are intensifying the complexity of structural components every day and assuring the structural integrity is becoming more essential, thus creating challenges for developing a cost effective and reliable non-destructive evaluation (NDE) technique. As conventional NDE techniques usually require expensive equipments, trained experts and out-of-service period, such techniques may be inadequate for autonomous online health monitoring of structures. In this study, a relatively new technique known as electromechanical impedance (EMI) technique is combined with a neural network technique to predict the damaged areas on a composite plate. Regardless of the advantages such as low cost, robustness, simplicity and online possibilities, this technique still has various problems to be solved. For one, locating a damaged area can be extremely difficult as this non-model based technique heavily relies on the variations in the impedance signatures. The results show that the non-homogenous property is an advantage for the study, successfully identifying the damage location for the prepared test specimen with an acceptable performance.