International Journal of Prognostics and Health Management

A Data-Driven Methodology to Assess Raw Materials Impact on Manufacturing Systems Breakdowns

2024-03-15T13:48:49+00:00

Data-driven Prognostics and Health Management (PHM) become a crucial layer in the realm of predictive maintenance (PM). However, many industries develop PM technologies based on the monitoring of machine data to anticipate failures without considering the injected raw material. In reality, non-compliant material characteristics can affect the manufacturing tools leading to machine breakdowns and poor quality product. To cope with this situation, this paper proposes a new methodology that helps operators predicting machine breakdowns. In detail, the methodology starts by implementing an Extract, Transform, Load (ETL) process which aims to create a new and reliable dataset from heterogeneous sources. Then, a feature selection method is used for dimensionality reduction and keep only useful information. After that, the selected features are injected to Machine Learning (ML) algorithms to predict system breakdown occurrences. Finally, the novelty in this study, an auto-labeling algorithm based on material data and machine breakdown predictions is proposed. This algorithm aims to enhance raw material stock management, scheduling their consumption accordingly and thus reducing machine breakdowns. The developed methodology is applied to a real dataset of a French company, SCODER, that shows and pointed out promising perspectives in PM.

A Deep Learning Approach to Within-Bank Fault Detection and Diagnostics of Fine Motion Control Rod Drives

2024-01-18T11:13:27+00:00

Control rod motion is one of the primary means of regulating the rate of fission in a nuclear reactor core to ensure safe and stable operation. Reactor power distribution and thermal power output can be fine-tuned by adjusting the control rod position. For high-precision control of rod movements, Fine Motion Control Rod Drives (FMCRDs) are often used. The operation of FMCRDs provides a unique opportunity to implement condition monitoring related to the intermittency of motion and the use of control rod banks. This research sets out to detect three types of faults in an electrically driven FMCRD. In addition to detecting faults, this work will attempt to determine both the type of fault and the source of each fault, completing the fault detection and diagnostics (FDD) pipeline on a scarcely researched system. The three types of faults to be investigated are short-circuit faults, ball screw wear faults, and ball screw jam faults. This is a potential advancement to the within-bank FDD of this specific drive system intended for deployment in an advanced nuclear reactor plant. Using encoder-decoder structured convolutional neural networks and autoencoders, the three tested faults were confidently detected and isolated as well as reasonably diagnosed by monitoring the FMCRD servomotor torque.

Prognostics Aware Control Design for Extended Remaining Useful Life

2024-02-13T09:16:38+00:00

As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

2023-12-27T19:54:07+00:00

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms.

In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well.

Post-clustering Prioritization Framework for Autonomous Decision Making in the Absence of Ground Truth via Hypothetical Probing

2023-07-24T10:31:47+00:00

A generic prioritization framework is introduced for addressing the problem of automated prioritization of region of interest or target selection. The framework is based on the assumption that clustering of preliminary data for preidentified regions or targets of interest within an operational area has already occurred, i.e., post-classification, and that the clustering quality can be expressed as an energy/objective function. Region or target of interest prioritization then means to rank regions or targets of interest according to their probability of changing the energy/objective function value upon subsequent hypothetical probing as opposed to actually conducted reexamination, i.e., thorough follow-up or in-situ measurements. The mathematical formalism for calculating these probabilities to contribute to this change of the energy/objective function value is introduced and validated through numerical simulations. Moreover, these probabilities can also be understood as a confidence-check of the classification, i.e., the pre-clustering of the preliminary data. The operation of the prioritization framework is independent of the algorithm used to pre-cluster the preliminary data, and supports autonomous decision-making. It is widely applicable across many scientific disciplines and areas, ranging from the microscopic to the macroscopic scale. Due to its ability to help maximize scientific return while optimizing resource utilization, it is particularly relevant in the context of resource-constrained autonomous robotic planetary exploration as it may extend the Remaining Useful Lifetime (RUL) – a key aspect in Prognostics and Health Management (PHM) – of space missions. On a more general, PHM-relevant level, the prioritization framework may provide an additional mechanism of identifying and correcting the maintenance status of system components to assist predictive maintenance or condition-based maintenance.

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

2024-02-23T13:16:22+00:00

This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.

Bearing Fault Diagnosis under Varying Work Conditions Based on Synchrosqueezing Transform, Random Projection, and Convolutional Neural Networks

2024-01-24T21:14:08+00:00

Bearings are critical components in rotating machinery, and their failure can lead to costly repairs and downtime. To prevent such failures, it is important to detect and diagnose bearing faults early. In recent years, deep-learning techniques have shown promise for detecting and diagnosing bearing faults automatically. While these algorithms can all achieve diagnostic accuracy of over 90%, their generalizability and robustness in complex, extreme variable loading conditions have not been thoroughly validated. In this paper, a feature extraction method based on Synchro-squeezing Wavelet Transform (SSWT), Random projection (RP), and deep learning (DL) is presented. To fulfill the data requirements of neural networks, data augmentation is initially utilized to augment the size of the original data. Subsequently, the SSWT technique is employed to convert the signals from the Time domain to the Time-Frequency domain, resulting in the conversion of the 1-D signal to a 2-D feature image. To decrease the complexity of deep learning computation, data preprocessing involves utilizing Random projection to reduce feature dimensionality. The final step involves constructing a Convolutional Neural Network (CNN) model that can identify fault features from the obtained Time-Frequency images and perform accurate fault classification. By utilizing the CWRU and IMS datasets to evaluate the method, the study demonstrates that the suggested approach outperforms advanced techniques in terms of both diagnostic accuracy and robustness.

An ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

2024-01-15T00:25:51+00:00

Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.

Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures

2024-02-07T15:35:08+00:00

The generation of chatter during machining operations is extremely detrimental to the cutting tool life and the surface quality of the workpiece. The present study aims to identify chatter conditions during the end milling of Ti6Al4V alloy. Experimental modal analysis is carried out, and stability lobe diagrams (SLDs) are developed to identify machining parameters under stable and chatter conditions. Experiments are conducted to acquire cutting force and vibration signatures corresponding to machining conditions selected from the SLD. Non-linear chatter features, such as Approximate Entropy, Holder Exponent, and Lyapunov Exponent extracted from the sensor signatures, are used to build Machine Learning (ML) models to identify chatter using Decision Trees (DTs), Support Vector Machines (SVMs) and DT-based Ensembles. A feature-level fusion approach is adopted to improve the classification performance of the ML models. The DT-based Adaboost model trained using dominant non-linear features classifies chatter with an accuracy of 96.8%. The non-linear features extracted from the sensor signatures offer a direct indication of the chatter and are found to be effective in identifying the machining chatter with good accuracy.

An Improved Fault Detection Method based on HSMM

2024-01-28T13:18:35+00:00

This paper proposes a fault detection method for multivariate statistical process control. The proposed method combines the Forward-Backward Hidden Semi-Markov Model (HSMM) and Principal Component Analysis (PCA). A stochastic automaton was used for multi-mode detection with many observation sequences. We used agglomerative clusters to find the initial parameters of HSMM. We allocated an adaptive threshold and a fixed threshold in each mode for fault detection with PCA, including Hotelling T2 statistic and squared predictive error (Q statistic). We simulated this method on the Tennessee Eastman Process (TEP). Some faults were designed with various runs and times of occurrence. The experimental results were compared with the Mixture Bayesian PCA, Hidden Markov Model (HMM), and HSMM methods. The results are robust with an efficient detection rate. This activity recommends ways to find action plans for multi-mode process monitoring in chemical plants.

A Multi-Output Deep Learning Model for Fault Diagnosis Based on Time-Series Data

2024-04-15T11:06:32+00:00

In this work, a method for fault diagnosis and localization is proposed. This method adopts the long short-term memory (LSTM) neural network to detect, isolate and determine the component of the system in which a fault has occurred. Unlike the traditional methods used for fault diagnosis, which first extract features from the raw data and then use a classifier in order to diagnose the fault; the LSTM-based method works directly on raw data and builds the classifier. This can be accomplished by training the neural network using the raw data resulting in a trained model (classifier) capturing generalized patterns from this data. This model is used online to diagnose faults and determine the faulty component. The performance of the resulting model is evaluated on testing data. The proposed method has been applied to real time-series data representing sensor readings in spacecraft electrical power distribution systems. The experimental results show promising performance in separating fault modes and identifying the faulty components.