Failure Modes Mechanisms Effects Analysis for Refrigeration Device

2013 ◽  
Vol 288 ◽  
pp. 69-74 ◽  
Author(s):  
Ren Xiao Xu ◽  
Yang Liu
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Life Cycle Management ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Automatic Identification ◽  
Health State ◽  
Useful Life ◽  
Using Data ◽  
Fusion Approach

FMMEA (failure mode, mechanisms, and effects analysis) is an effective tool for the life-cycle management of products and devices. We conducted an FMMEA for a refrigeration device at the request of a corporation. This paper demonstrates the process of our analysis of the compressor by employing Ganesan’s methodology. The results are listed in a table, including the physics of failures, risk priorities and parameters for monitoring. This paper also provides health-state assessment approaches based on FMMEA results and values of relevant parameters using fusion approach. Such assessment can be used for remaining useful life (RUL) estimation. Additionally, the paper illustrates our approach of computer-program-based automatic identification of failure using data of parameters retrieved from sensors.

scholarly journals Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Federico Barbieri ◽  
J. Wesley Hines ◽  
Michael Sharp ◽  
Mauro Venturini
Keyword(s):  
Failure Modes ◽  
Model Performance ◽  
Optimization Methods ◽  
Ordinary Least Squares ◽  
Remaining Useful Life ◽  
Path Model ◽  
Health State ◽  
Prognostic Parameter ◽  
Accelerated Degradation ◽  
Useful Life

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.

Remaining useful life prediction of rolling element bearings based on health state assessment

2015 ◽  
Vol 230 (2) ◽  
pp. 314-330 ◽  
Author(s):  
Zhiliang Liu ◽  
Ming J Zuo ◽  
Yong Qin
Keyword(s):  
Regression Model ◽  
Hybrid Approach ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health State ◽  
Rolling Element Bearings ◽  
Local Regression ◽  
Rolling Element ◽  
Useful Life

Instead of looking for an overall regression model for remaining useful life (RUL) prediction, this paper proposes a RUL prediction framework based on multiple health state assessment that divides the entire bearing life into several health states where a local regression model can be built individually. A hybrid approach consisting of both unsupervised learning and supervised learning is proposed to automatically estimate the real-time health state of a bearing in cases with no prior knowledge available. Support vector machine is the main technology adopted to implement health state assessment and RUL prediction. Experimental results on accelerated degradation tests of rolling element bearings demonstrate the effectiveness of the proposed framework.

scholarly journals Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres
Keyword(s):  
Deep Learning ◽  
Latent Variables ◽  
Latent Variable ◽  
Failure Modes ◽  
Remaining Useful Life ◽  
Space Representation ◽  
Health State ◽  
Partial Differential ◽  
Unseen Data ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

scholarly journals Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402097249
Author(s):  
Liming Li ◽  
Xunyi Zhou ◽  
Xingqi Zhang ◽  
Zhenghu Zhong
Keyword(s):  
Markov Model ◽  
Prediction Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Vibration Signal ◽  
Remaining Useful Life ◽  
Health State ◽  
Starting Point ◽  
Useful Life ◽  
Position Precision

In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.

Health State Assessment and Lifetime Prediction Based on Unsupervised State Estimation

2019 ◽  
Author(s):  
Rosmawati Jihin ◽  
Dirk Söffker
Keyword(s):  
State Estimation ◽  
Clustering Algorithm ◽  
State Machine ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Hydraulic Pressure ◽  
Health State ◽  
Estimation Process ◽  
Lifetime Model

Abstract Assessment of system health and prediction of remaining useful life can be performed effectively through the evaluation of degradation levels configured by multiple states. Commonly, degradation progression is modeled according to the specific configuration using existing algorithms with assuming numbers and state conditions. However, due to the complexity, especially in the case of a system with multiple hidden states, the proper configuration is hard to assign and to identify. The need for unsupervised state estimation process to assist degradation modeling preventing under or over assumption becomes obvious. Among the existing approaches is the application of clustering methods to classify data and to estimate the number of degradation states might exist. However, integration into the lifetime prediction framework is still infancy and often not considered. Therefore, in this work, a previously developed state machine lifetime model is extended to allow flexibility in configuring state topology based on K-means clustering algorithm and cluster validity index for the optimal number of states identification. Combining unsupervised state estimation process with a new state machine lifetime model has transformed it into a semi-supervised prognostic approach. For validation, hydraulic pressure data from tribology experiment are deployed for training and test the algorithm. Based on the evaluation, this approach demonstrates the ability to improve health state assessment and lifetime prediction in a more flexible way to address the variability in the system.

scholarly journals Prognosis of Water Quality Sensors Using Advanced Data Analytics: Application to the Barcelona Drinking Water Network

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1342 ◽  
Author(s):  
Diego Garcia ◽  
Vicenç Puig ◽  
Joseba Quevedo
Keyword(s):  
Water Quality ◽  
Distribution System ◽  
Data Analytics ◽  
Water Distribution ◽  
Discrete Event ◽  
Spatial Models ◽  
Remaining Useful Life ◽  
Health State ◽  
Using Data ◽  
Water Quality Sensors

Water Utilities (WU) are responsible for supplying water for residential, commercial and industrial use guaranteeing the sanitary and quality standards established by different regulations. To assure the satisfaction of such standards a set of quality sensors that monitor continuously the Water Distribution System (WDS) are used. Unfortunately, those sensors require continuous maintenance in order to guarantee their right and reliable operation. In order to program the maintenance of those sensors taking into account the health state of the sensor, a prognosis system should be deployed. Moreover, before proceeding with the prognosis of the sensors, the data provided with those sensors should be validated using data from other sensors and models. This paper provides an advanced data analytics framework that will allow us to diagnose water quality sensor faults and to detect water quality events. Moreover, a data-driven prognosis module will be able to assess the sensitivity degradation of the chlorine sensors estimating the remaining useful life (RUL), taking into account uncertainty quantification, that allows us to program the maintenance actions based on the state of health of sensors instead on a regular basis. The fault and event detection module is based on a methodology that combines time and spatial models obtained from historical data that are integrated with a discrete-event system and are able to distinguish between a quality event or a sensor fault. The prognosis module analyses the quality sensor time series forecasting the degradation and therefore providing a predictive maintenance plan avoiding unsafe situations in the WDS.

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