Prognostics and Health Monitoring of Electro-Hydraulic Systems

2017 ◽  
Author(s):  
Damoon Soudbakhsh ◽  
Anuradha M. Annaswamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Hydraulic Systems ◽  
Multiple Sources ◽  
Redundant Actuators ◽  
Component Failures ◽  
Prognostics And Health Monitoring

Electro-Hydraulic Systems (EHS) are commonly used in many industrial applications. Prediction and timely fault detection of EHS can significantly reduce their maintenance cost, and eliminate the need for redundant actuators. Current practice to detect faults in the actuators can miss failures with combination of multiple sources. Missed faults can result in sudden, unforeseen failures. We propose a fault detection technique based on Multiple Regressor Adaptive Observers (MRAO). The results were evaluated using a two-stage servo-valve model. The proposed MRAO can be used for on-line fault detection. Therefore, we propose a health monitoring approach based on the trend of the identified parameters of the system. Using the history of identified parameters, normal tear and wear of the actuator can be distinguished from the component failures to more accurately estimate the remaining useful life of the actuator.

scholarly journals Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

scholarly journals Design and Development of Techniques for Equipment Health Monitoring System

2019 ◽  
Vol 9 (2S) ◽  
pp. 277-282
Keyword(s):  
Fault Detection ◽  
Embedded System ◽  
Health Monitoring ◽  
Conveyor Belt ◽  
Unmanned Vehicles ◽  
Sensor Data ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Markovian Model ◽  
Time Data

Machines in Industries are often subjected to enormous wear and tear, which if unnoticed, may lead to production delays and increased maintenance cost. Machines must be able to analyse and provide statistics about its health, so that preventive measures can be taken to avoid catastrophes in the industries. Thus, there is a need of automated fault detection and prediction of system’s condition. The concept of equipment health monitoring is a crucial step in the field of research and development in the manufacturing industries. This equipment makes it handy in situations where machines require continuous monitoring and is difficult for humans to provide such attention , especially in the case of unmanned vehicles. Prediction of the status of equipment by acquisition of data from industrial machinery is the critical step in building such a system. Health of machines can be estimated by the data collected by the sensors-temperature, accelerometer, etc integrated with an embedded computing system, like a Raspberry Pi. This IoT model consisting of embedded system with wireless connectivity collects real time data from the equipment/machinery used in industries. This data is used to analyse and predict the health of the equipment, examine the steady-state characteristics using Machine Learning technique, Hidden Markovian Model. The concept of the proposed IoT model is evaluated over a conveyor belt test rig under various conditions, like different loads placed on various locations of conveyor belt and the belt is made to run at different speeds and data is collected over all these conditions. Then, a data model is created using Hidden Markovian Model which is further used in predicting the state of the belt based on the sequential data, here it is the sensor data. Given a state of the belt, this model can predict whether the belt is in proper condition or not, and if human intervention is required. Thus, at any point of time, having this setup on the machinery which needs to be monitored can be used in predicting the faults and notifying the user in case of any faulty behaviour or malfunctioning of machines. This setup can be used for any machines which are subjected to any motion, vibration and thermal changes. This helps in creating a completely automated fault detection systems in the present Industries.

scholarly journals Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 747 ◽  
Author(s):  
Bo Wu ◽  
Yangde Gao ◽  
Songlin Feng ◽  
Theerasak Chanwimalueang
Keyword(s):  
Health Monitoring ◽  
Hybrid Method ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Least Square ◽  
Qr Factorization ◽  
Maintenance Cost ◽  
Minimum Entropy ◽  
Useful Life

To reduce the maintenance cost and safeguard machinery operation, remaining useful life (RUL) prediction is very important for long term health monitoring. In this paper, we introduce a novel hybrid method to deal with the RUL prediction for health management. Firstly, the sparse reconstruction algorithm of the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) is applied to compressed sensing (CS), which can realize the sparse optimization for long term health monitoring data. After the sparse signal is reconstructed, the minimum entropy de-convolution (MED) is used to identify the fault characteristics and to obtain significant fault information from the machinery operation. Health indicators with Skip-over, sample entropy and approximate entropy are then performed to track the degradation of the machinery process. The performance analysis of the Skip-over is superior to other indicators. Finally, Fractal Autoregressive Integrated Moving Average model (FARIMA) is employed to predict the Skip-over using the R/S method. The analysis results evidence that the novel hybrid method yields a good performance, and such method can achieve highly accurate RUL prediction and safeguard machinery operation for long term monitoring.

scholarly journals Real-Time Maintenance Optimization Considering Health Monitoring and Additive Manufacturing

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Adrian Cubillo ◽  
Suresh Perinpanayagam ◽  
Manuel Esperon-Miguez ◽  
Philip John
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Health Monitoring ◽  
Health Management ◽  
Critical Factor ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Availability Constraints ◽  
Gradual Introduction ◽  
The Impact

Prognostics Health Management (PHM) and Integrated Vehicle Health Management (IVHM) are extensive areas of research. Whereas a lot of work has been done in diagnostics and prognostics, economic viability is an important consideration. The availability of aircraft in the aerospace sector is a critical factor. Thus, cost and downtime are the main parameters to assess the impact of IVHM. Additionally, new repair technologies, such as additive manufacturing (AM), have the potential to become standard repair procedures, complementing IVHM, and its viability also has to be assessed. However, to accurately study the impact of these factors, the characteristics of aerospace maintenance have to be taken into account. Several approaches are followed in aircraft maintenance, depending on cost, downtime and aircraft availability constraints. For instance, some parts can be repaired on the ground and assembled again on the same aircraft, while single Line Replaceable Units (LRUs) need to be removed, replaced and later repaired in the workshop without affecting the availability of the aircraft. With the gradual introduction of IVHM, the viability of any new IVHM technology needs to be assessed.This paper describes an extensive cost and downtime model to take into account all these scenarios, including the impact of using different types of IVHM systems. The impact of IVHM and new repair technologies is discussed comparing maintenance cost and downtime of parts of LRUs and parts repaired when the aircraft is on the ground.Secondly, a real-time maintenance case study based on IVHM, a cost and downtime model and additive manufacturing is presented. This application allows the optimization of maintenance activities by updating the available resources and their corresponding cost and time, along with the actual prediction of the Remaining Useful Life (RUL) using a health monitoring system, instead of depending on historical component/sub-system failure probabilities.

scholarly journals Optimal Cost-Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Xin Li ◽  
Jing Cai ◽  
Hongfu Zuo ◽  
Huaiyuan Li
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Remaining Useful Life ◽  
Replacement Policy ◽  
Detection Methods ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Bayesian Control ◽  
Early Fault Detection ◽  
Optimal Maintenance Policy

Most of the existing fault detection methods rarely consider the cost-optimal maintenance policy. A novel multivariate Bayesian control approach is proposed, which enables the implementation of early fault detection for a helicopter gearbox with cost minimization maintenance policy under varying load. A continuous time hidden semi-Markov model (HSMM) is employed to describe the stochastic relationship between the unobservable states and observable observations of the gear system. Explicit expressions for the remaining useful life prediction are derived using HSMM. Considering the maintenance cost in fault detection, the multivariate Bayesian control scheme based on HSMM is developed; the objective is to minimize the long-run expected average cost per unit time. An effective computational algorithm in the semi-Markov decision process (SMDP) framework is designed to obtain the optimal control limit. A comparison with the multivariate Bayesian control chart based on hidden Markov model (HMM) and the traditional age-based replacement policy is given, which illustrates the effectiveness of the proposed approach.

scholarly journals International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Ali Ashasi-Sorkhabi ◽  
Stanley Fong ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Health Management ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Vibration Data ◽  
Condition Indicators ◽  
Failure Data ◽  
Wide Range

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.

Towards prognostics and health monitoring: The potential of fault detection by piezoresistive silicon stress sensor

2017 ◽  
Vol 74 ◽  
pp. 165-172 ◽  
Author(s):  
Alicja Palczynska ◽  
Alexandru Prisacaru ◽  
Przemyslaw Jakub Gromala ◽  
Bongtae Han ◽  
Dirk Mayer ◽  
...  
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Stress Sensor ◽  
Prognostics And Health Monitoring

scholarly journals Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Steffen Haus ◽  
Heiko Mikat ◽  
Martin Nowara ◽  
Surya Teja Kandukuri ◽  
Uwe Klingauf ◽  
...  
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Supply ◽  
Research Work ◽  
Induction Machines ◽  
Industrial Applications ◽  
Aviation Industry ◽  
Bearing Fault ◽  
Motor Design ◽  
Electrical Induction

Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed. A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA) based on a Fast Fourier Transform (FFT). Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications. In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal. Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted withrespect to the motor design. Physical interpretation are given in case the results differ from those found in literature for 50Hz electrical machines.

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