International Journal of Prognostics and Health Management

Observation and Prediction of Instability due to RD Fluid Force in Rotating Machinery by Operational Modal Analysis

2024-05-12T15:29:46+00:00

In recent years, as rotating machinery has become smaller and more efficient, various types of shaft vibration problems have arisen. Failure of rotating machinery may lead to major accidents and infrastructure shutdowns. Therefore, to prevent failures of rotating machinery, there is a growing need for the vibration analysis technology at the design stage and condition monitoring during operation stage. One of causes of the shaft vibration problems in rotating machinery is the rotordynamic (RD) fluid force acting on fluid elements such as journal bearings, seals, turbine blades, and so on. RD fluid force has a significant effect on the stability of rotating machinery and can destabilize the system. In recent years, operational modal analysis (OMA) methods, which identify modal parameters based on the measured data of a machine's operational condition, have been investigated in the condition monitoring. In this paper, the estimation of the modal parameters of rotating machinery using OMA from only the time history response of displacement data and, in particular, the prediction of the destabilization of rotating machinery caused by RD fluid force are investigated. As a result, the modal parameters are well estimated and, in particular, the destabilization of one mode due to RD fluid force is predicted and explained. The results are in good agreement with the results of the eigenvalue analysis of the original system, and the method is validated. Furthermore, the proposed method is applied to experimental data of the system destabilized by fluid force. The change in stability with rotational speed is observed, and the characteristics of the mode toward destabilization are confirmed. The results show the validity of OMA's predictions of destabilization in the experiments.

An Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

2024-06-16T13:41:13+00:00

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

Real-Time Detection of Internal Short Circuits in Lithium-Ion Batteries using an Extend Kalman Filter

2024-06-04T17:10:49+00:00

Concerns over fuel scarcity and environmental degradation largely drive the increasing popularity of electric vehicles (EVs). Lithium-ion batteries (LIBs), known for their high energy and power densities, are the favored power source for EVs. Over the past few decades, research has been concentrated on ensuring these batteries operate efficiently, safely, and reliably. A key issue impacting the safety of Li-ion batteries is thermal runaway (TR), which can lead to hazardous battery fires. Internal short circuits (ISCs) are often the primary cause of these TR incidents, making the early detection of spontaneous ISC formation a pivotal diagnostic task. This research introduces an innovative ISC detection technique for cylindrical Li-ion battery cells. This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. This framework enables real-time estimation of the internal ISC state while maintaining computational efficiency. The proposed method is tested numerically considering a high-fidelity numerical plant cycled using charge-depleting tests that mimic a practical battery cell working cycle at various C rates and at different ambient temperatures to account for both load and environmental uncertainties. The results demonstrate the robustness and effectiveness of the method. In addition, the method has been proven to be computationally efficient, demonstrating the feasibility of its real-time implementation.

Efficient Differential Diagnosis using Cost-aware Active Testing

2024-06-29T15:22:12+00:00

The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing, and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes, considering costs of actions to minimize the expected overall cost of the diagnosis.
We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

2024-08-17T11:05:12+00:00

The accurate measurement of partial discharges (PD) in power transformers is a critical component in identifying faults and planning effective maintenance strategies. Acoustic emission (AE) sensing technology is suitable for detecting partial discharges in transformers because it is less affected by external electromagnetic interference and detects PD non-invasively. This paper delves into examining the relationship between the detection intensity, the specific type of PD source, and the distance to the discharge source, using AE sensors. It was observed that AE wave intensities from corona discharges tend to be relatively strong, indicating a higher detection probability. Creepage discharges usually exhibit the next level of intensity, followed by the PD occurring in bubbles. It is considered that the intensity of the AE waves varies significantly depending on both the speed at which the discharge propagates and the medium and volume of the discharge space. Furthermore, this study has conducted experimental comparisons among three distinct methods for calculating the Time Difference of Arrival (TDOA) in localization calculations. These methods are the energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The experimental results suggest that the energy criterion method is particularly effective when sensors are distributed placement around the entire tank. In contrast, the GCC-PHAT method shows greater suitability in scenarios where sensors are centralized placement in certain sections of the tank. It was clearly observed that the GCC-PHAT method, which suppresses the impact of noise and reflections, consistently achieves higher estimation accuracy in comparison to the standard GCC method. Notably, this method has shown the ability to maintain its accuracy levels even in cases of low discharge intensities. The implications of these findings are significant, as they could improve the precision and effectiveness of maintenance diagnostics in power transformers.

Anomaly Detection in Time Series Data

2024-06-15T14:58:02+00:00

This paper presents a novel unsupervised approach for anomaly detection in marine diesel engines using a Transformer Neural Network based autoencoder (TAE) and residual analysis with Sequential Probability Ratio Test (SPRT) and Sum of Squares of Normalized Residuals (SSNR). This approach adeptly captures temporal dependencies within normal time-series data without the necessity for labeled failure data. To assess the performance of the proposed methodology, a dataset containing faulty data is generated under the same operational profile as the normal training data. The model undergoes training using normal data, after which the faulty data is reconstructed utilizing the trained model. Subsequently, SPRT and SSNR are used to analyze the residuals from the observed and reconstructed faulty data. Significant deviations surpassing a predefined threshold are identified as anomalous behavior. Additionally, this study explores various architectures of Transformer neural networks and other types of neural networks to conduct a comprehensive comparative analysis of the performance of the proposed approach. Insights and recommendations derived from the performance analysis are also presented, offering valuable information for potential users to leverage. The experimental results demonstrate that the proposed approach can accurately and efficiently detect anomalies in marine diesel engines. Therefore, this approach can be considered as a promising solution for early anomaly detection, leading to timely maintenance and repair, and preventing costly downtime. Also, avoid accidents with probability severe consequences.

A new approach to multivariate statistical process control and its application to wastewater treatment process monitoring

2024-05-12T15:22:30+00:00

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate Statistical
Process Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. Firstly,
a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand user
interface and then a new yet simplified reconfigurable diagnostic model is introduced. The user interface that has been
developed is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trend
graphs. The proposed diagnostic model is constructed by aggregating small models with either one or two inputs, which
enhances the tractability of the diagnostic model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment.
plant.

Automatic Detection of Concrete Surface Defects Using PRE-TRAINED CNN and Laser Ultrasonic Visualization Testing

2024-08-01T22:30:30+00:00

In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens.

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

2024-07-31T13:28:25+00:00

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

Towards Explainable Anomaly Detection in Safety-critical Systems

2024-09-17T13:30:52+00:00

Ensuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes. This paper introduces a novel methodology for the detection of systemic anomalies in the telemetry data of the International Space Station (ISS), leveraging the synergistic application of the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL). Integrated with machine learning-based normal behavior prediction model, this approach significantly enhances the explanatory of anomaly detection mechanisms. The methodology is verified and validated through its application to the thermal control system within the ISS's Japanese Experimental Module (JEM), illustrating its capacity to augment diagnostic capabilities and assist flight controllers and specialists in preserving the ISS's functional integrity. The findings underscore the importance of explainability in the machine learning-based anomaly detection of safety-critical systems and suggest a promising avenue for future explorations aimed at bolstering space system health management through improved explainability and operational efficiency.

Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System

2024-09-20T23:27:27+00:00

Autonomous Centerline Tracking (ACT) enables an unmanned aircraft to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. The EASA Guidelines for machine-learning based systems list numerous assurance objectives that must be met toward certification and V&V. We extend our statistical analysis framework SYSAI to support meeting assurance objectives for a complex safety-critical system with AI components, and describe a combination with a runtime monitoring architecture that also supports advanced risk mitigation to support safety assurance of a complex AI-based aerospace system.

A Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems

2024-08-19T13:22:09+00:00

The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.

Editorial for IJPHM Special Issue on PHM Asia Pacific Conference 2023

2024-10-07T12:30:15+00:00

Prognostics and system health management (PHM) remain crucial for maintaining various industrial systems' reliability, safety, and efficiency. However, there remain gaps and challenges that need to be addressed. The special issue compiles 12 extended papers from the 2023 PHM Asia Pacific Conference, showcasing extensive research and a wide range of applications. It showcases research findings on autonomous system monitoring, fault detection, anomaly detection, and predictive maintenance. A common theme across the papers is the balance between computational efficiency, explainability, and real-world applicability. While the research presented here is highly innovative, it also highlights the need for PHM systems that are not only robust and scalable but also transparent and easily interpretable for end-users.