A novel prognostics solution for machine tool sub-units: The hydraulic case

2021 ◽  
pp. 095440542110646
Author(s):  
Luca Bernini ◽  
David Waltz ◽  
Paolo Albertelli ◽  
Michele Monno
Keyword(s):  
Machine Tool ◽  
Working Conditions ◽  
Robustness Analysis ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Classification Model ◽  
Prediction Errors ◽  
Classification Rate ◽  
Hydraulic Unit ◽  
Digital Twin

A novel prognostic approach was developed and applied to a machine tool hydraulic unit. Three components were considered: pump, sensor and valve. The proposed methodology exploited a digital twin of the system to perform simulations of the healthy and faulty machine. The digital twin was properly validated through experiments. This approach dealt with the need to carry out time-consuming and expensive experimental campaigns, that is, run-to-failures – not affordable in many industrial applications. The diagnosis module was trained on digital twin simulations and fulfilled the fault detection, isolation and quantification phases. The challenge related to the variability of the operating conditions of the machine was addressed through a robustness analysis of the methodology. The solution successfully dealt with both stationary and non-stationary working conditions. A dedicated classification model was designed for each faulty component, maximising the associated classification rate. The testing procedure consisted of the application of a 10-fold cross-validation to compute the mean classification rates for stationary and non-stationary working conditions. Diagnosis performance results were excellent for the pump, whereas they were lower for the sensor and valve, reaching 79.75% and 74.93% accuracy respectively for the most challenging working cycle. The prognosis directly exploited the output of diagnostics, allowing for experimental effort reduction. Prognosis predictions were built starting from the updated health status provided by the diagnosis output. In order to test the prognosis module, mean and standard deviation of the prediction errors (less than 1.176%) were computed through a Monte Carlo approach. The conceived methodology allowed one of the critical goals of prognostics to be handled: the Remaining Useful Life probability density function estimation.

scholarly journals Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 49 ◽  
Author(s):  
Faisal Khan ◽  
Omer Eker ◽  
Atif Khan ◽  
Wasim Orfali
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Health Management ◽  
Cost Effective ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Prediction Errors ◽  
Degradation Model ◽  
Jet Engine

In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.

scholarly journals Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Chenyu Liu ◽  
Alexandre Mauricio ◽  
Junyu Qi ◽  
Dandan Peng ◽  
Konstantinos Gryllias
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Real Data ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Simulation Data ◽  
Digital Twin ◽  
Physical Knowledge ◽  
Knowledge Based

Artificial Intelligence (AI) is escalating in data-driven condition monitoring research. Traditional expert knowledge-based Prognostics and Health Management (PHM) processes can be smartened up with the assistance of various AI techniques, such as deep learning models. On the other hand, current deep learning based prognostics suffers from the data deficit issue, especially considering the varying operating conditions and the degradation modes of the components in practical industrial applications. With the development of simulation techniques, physical-knowledge based digital twin models give engineers access to a large amount of simulation data at a lower cost. These simulation data contain the physical characteristics and the degradation information of the component. In order to accurately predict the Remaining Useful Life (RUL) during the degradation process, in this paper, a bearing digital twin model is constructed based on a phenomenological vibration model. A Domain Adversarial Neural Network (DANN) is used to achieve the domain adaptation target between the simulation and the real data. Regarding the simulation data as the source domain and real data as the target domain, the DANN model is able to predict the RUL without any priori knowledge of the labelling information. Based on real bearing run-to-failure experiments, the performance of the proposed method is validated with high RUL prediction accuracy.

scholarly journals Core Stress Analysis of Amorphous Alloy Transformer for Rail Transit under Different Working Conditions

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 164
Author(s):  
Jianwei Shao ◽  
Cuidong Xu ◽  
Ka Wai Eric Cheng
Keyword(s):  
Amorphous Alloy ◽  
Working Conditions ◽  
Short Circuit ◽  
Element Model ◽  
Operating Conditions ◽  
Iron Core ◽  
Rail Transit ◽  
Transit System ◽  
The Core ◽  
Distribution Transformers

The rail transit system is a large electric vehicle system that is strongly dependent on the energy technologies of the power system. The use of new energy-saving amorphous alloy transformers can not only reduce the loss of rail transit power, but also help alleviate the power shortage situation and electromagnetic emissions. The application of the transformer in the field of rail transit is limited by the problem that amorphous alloy is prone to debris. this paper studied the stress conditions of amorphous alloy transformer cores under different working conditions and determined that the location where the core is prone to fragmentation, which is the key problem of smoothly integrating amorphous alloy distribution transformers on rail transit power supply systems. In this study, we investigate the changes in the electromagnetic field and stress of the amorphous alloy transformer core under different operating conditions. The finite element model of an amorphous alloy transformer is established and verified. The simulation results of the magnetic field and stress of the core under different working conditions are given. The no-load current and no-load loss are simulated and compared with the actual experimental data to verify practicability of amorphous alloy transformers. The biggest influence on the iron core is the overload state and the maximum value is higher than the core stress during short circuit. The core strain caused by the side-phase short circuit is larger than the middle-phase short circuit.

A Stochastic Approach to Characterization of Machine Tool System Dynamics Under Actual Working Conditions

1976 ◽  
Vol 98 (2) ◽  
pp. 614-619 ◽  
Author(s):  
F. A. Burney ◽  
S. M. Pandit ◽  
S. M. Wu
Keyword(s):  
Machine Tool ◽  
Working Conditions ◽  
Stochastic Approach ◽  
Relative Displacement ◽  
Process Dynamics ◽  
Actual Working ◽  
Machine Tool Structure ◽  
Vertical Milling ◽  
Vertical Milling Machine ◽  
Tool Dynamics

The machine tool dynamics is evaluated under actual working conditions by using a time series technique. This technique develops mathematical models from only one signal, viz., the relative displacement between the cutter and the workpiece. Analysis of the experimental data collected on a vertical milling machine indicates that the new methodology is capable of characterizing the machine tool structure and the cutting process dynamics separately. Furthermore, it can also detect and quantify the interaction between these two subsystems.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

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