Effective Condition Monitoring Research Articles

Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

A parallel and multi-scale probabilistic temporal convolutional neural networks for forecasting the key monitoring parameters of gas turbine

As a crucial equipment in the power industry, gas turbines need effective condition monitoring techniques to maintain safe and reliable operations. Fast yet accurate long-term forecasting of the key monitoring parameters of gas turbine is vital for achieving the overall effective condition monitoring. This however poses challenges in terms of both efficacy and efficiency for conventional time series models like recurrent neural networks (RNN), since they run in a sequential manner. To this end, this paper introduces a novel parallel probabilistic time series prediction model. Particularly, the proposed model leverages the parallelism of temporal convolutional neural network (TCN) and exploits the power of latent space in the Bayesian framework. Besides, a multi-scale feature extraction strategy based on dense connections and a lagged observation strategy are presented to improve the model performance. In the comparative study against state-of-the art competitors on four classical system identification benchmarks, the proposed model significantly reduces the training time, while consistently achieving highly accurate predictions and providing appropriate uncertainty quantification. Thereafter, the proposed model is adopted to build a systematic workflow for the fast yet accurate long-term forecasting of the temperature of blade channel of gas turbine. The comprehensive results again highlight the superiority of the proposed model in terms of both the prediction quality and the training efficiency.

Frequency Spectrum Analysis of Magnetic Field Strength for Effective Condition Monitoring of Magnetic Cores

Frequency Spectrum Analysis of Magnetic Field Strength for Effective Condition Monitoring of Magnetic Cores

Local fusion generative adversarial network with dual-discriminator and parallel multipath and its application in machinery fault diagnosis with imbalanced data

Abstract Diagnosing faults in critical machinery components is imperative for effective condition monitoring and real-world datasets often suffer from data imbalance. To address this issue, numerous data generation methods have been developed, such as improved local fusion generative adversarial network (ILoFGAN), variational autoencoding GAN (VAEGAN), etc. However, the existing data generation methods primarily concentrate on global and single-scale features and often ignore local or multi-scale features, which leads to the omission of key features or nuances in the generated data. Therefore, a novel approach called the Local Fusion Generative Adversarial Network with Dual-discriminator and Parallel multipath (LoFGAN-DP) is designed to enhance the fault diagnosis performance in the context of imbalanced data. The LoFGAN-DP features a Parallel Multi-Path (PMP) module along with a dual-discriminator scheme, in which the multipath module facilitates feature extraction at various scales through convolution across paths of diverse sizes, and the dual-discriminator scheme can better improve the quality and diversity of the samples generated by the generator. The PMP module and dual-discriminator scheme enhance the proposed method's robustness against variations in input data. After generating data by LoFGAN-DP, a two-dimensional capsule network is further used to achieve the efficient recognition of fault features. To validate the proposed LoFGAN-DP in the machinery fault diagnosis with imbalanced data, the gear dataset and the self-constructed bearing dataset were utilized. Experimental results show that LoFGAN-DP significantly improves Structural Similarity Index (SSIM), Fréchet Inception Distance (FID), and fault classification accuracy compared to several advanced methods.

Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery.

Lubrication condition monitoring of journal bearings in diesel engine based on thermoelectricity

There is still a lack of effective lubrication condition monitoring methods in the field of diesel engines. The paper proposes a novel thermoelectric approach to divide the lubrication state of bearings. First, the generation mechanism of thermoelectric potential on bearings is clarified. Then, both experimental and simulation studies are done, and a strong correlation between lubrication and thermoelectric potential is shown. The film thickness and temperature are further confirmed as significant factors influencing thermoelectric potential. Generally, the thermoelectric potential increases with temperature. However, a small film thickness ratio (when the film thickness ratio is less than 4) will suppress the thermoelectric potential. Three typical lubrication states of bearings are distinguished through thermoelectric potential and supported by the Stribeck curve results. Moreover, the significant influence of lubrication on the bearing is confirmed through the analysis of surface morphology and composition.

Dissolved gas analysis comparison of electrically stressed methyl ester and mineral oil

Methyl ester is considered one of the alternative substitutes to mineral oil as an insulating liquid. This study investigates the dissolved gas analysis (DGA) of the methyl ester derived from palm oil, under low energy discharge faults. The aims are to understand the gas composition and evaluate the applicability of the well-established fault interpretation methods for mineral oil to the methyl ester. Experimental procedures were conducted based on the International Electrotechnical Commission (IEC) standards. It involved simulating electrical breakdowns in laboratory conditions as per IEC-156 standard and analyzing gas samples using gas chromatography based on IEC-567. Results show that methyl ester oils produce similar types of gases as mineral oils but at higher concentrations. The interpretation of DGA results using fault identification methods such as Duval Triangle, Duval Pentagon, and IEC ratio indicates an overestimation of fault severity in methyl ester oils, and categorizing the faults as high energy discharge. However, the key gas method correctly identifies the discharge in both methyl ester and mineral oils. These findings suggest the need for adjustments in existing DGA methods to account for the higher gas concentrations in methyl ester oils, for effective condition monitoring and maintenance of transformers if it was filled with methyl ester oil.

Statistical and sensitivity analysis of ultrasound signals for effective condition monitoring of electro-motors using industrial approach

This paper researches the importance of ultrasound methodology for swiftly detecting faults in electric motors and rotating machines. The primary focus of this research is on the intricate signal processing of ultrasound signals from both faulty and fault-free electro-motors. The principal goal is to conduct a comprehensive statistical investigation into signal factors, examining the effects of defect progression on the factors associated with continuously operating faulty electro-motors. In addition to the statistical analysis, this study explores the envelope-frequency spectrum of the signal under both healthy and defective conditions, employing the envelope method alongside Hilbert transformation. The objective is to thoroughly scrutinize the dynamic changes in ultrasound waveform and envelope spectrum of defective states, considering diverse degrees of defect severity over an extended time span. Moreover, the paper meticulously tracks the trajectory of factor changes over a 40-day operational period of a defective electro-motor. Additionally, the study delves into the sensitivity of the ultrasound method to impulse-wise shocks, which are recurrently observed in ultrasound signals, leading to deviations in certain signal factors from their established healthy thresholds. In response to this challenge, this paper conducts a particular analysis of signal factor sensitivity to impulse-wise noises, identifying robust factors that serve as reliable tools for firm condition monitoring. These identified factors are then presented as invaluable contributors to ensuring the precision and reliability of condition monitoring, especially in the presence of disruptive impulse-wise noises.

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

Tacho-less spur gear condition monitoring at variable speed operation using the adaptive application of variational mode extraction

Effective condition monitoring of machine components contributes to a safer working environment for operators and assists in averting critical machinery shutdowns. In real-world industrial scenarios, fault detection must remain simplified, user-friendly and robust despite speed variations. The operational demands of machinery frequently result in speed fluctuations, leading to spectral smearing, thereby compromising the efficiency of the analysis methodology. Furthermore, the intricacies of parameter initialisation often increase the complexity of the analysis methods. This study introduces a fault diagnosis algorithm that requires minimal initialisation and operates independently of tacho pulses. The algorithm proposed in the study incorporates variational mode extraction with the maxima tracking algorithm for instantaneous frequency estimation. Hankel matrix-based selective spectral fusion is proposed to mitigate the impact of frequency tracking errors caused by transient noise. The results of spectral side-band-based fault severity analysis, conducted on an in-house spur gearbox test-bed with seeded tooth chips, underscore the superior performance of the proposed algorithm when compared to contemporary non-stationary analysis methods.

A semantic framework for condition monitoring in Industry 4.0 based on evolving knowledge bases

In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Ontologies are used to structure knowledge in knowledge bases, which also contain instances and information about these data. Thus, a knowledge base provides a sort of virtual representation of the different elements involved in a manufacturing process. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must provide a way to represent this evolution in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This paper proposes a semantic framework to address the evolution of knowledge bases for condition monitoring in Industry 4.0. To this end, firstly we propose a semantic model (the COInd4 ontology) for the manufacturing domain that represents the resources and processes that are part of a factory, with special emphasis on the context of these resources and processes. Relevant situations that combine sensor observations with domain knowledge are also represented in the model. Secondly, an approach that uses stream reasoning to detect these situations that lead to potential failures is introduced. This approach enriches data collected from sensors with contextual information using the proposed semantic model. The use of stream reasoning facilitates the integration of data from different data sources, different temporal resolutions as well as the processing of these data in real time. This allows to derive high-level situations from lower-level context and sensor information. Detecting situations can trigger actions to adapt the process behavior, and in turn, this change in behavior can lead to the generation of new contexts leading to new situations. These situations can have different levels of severity, and can be nested in different ways. Dealing with the rich relations among situations requires an efficient approach to organize them. Therefore, we propose a method to build a lattice, ordering those situations depending on the constraints they rely on. This lattice represents a road-map of all the situations that can be reached from a given one, normal or abnormal. This helps in decision support, by allowing the identification of the actions that can be taken to correct the abnormality avoiding in this way the interruption of the manufacturing processes. Finally, an industrial application scenario for the proposed approach is described.

A framework for the practical development of condition monitoring systems with application to the roller compactor

Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems. Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance.

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.

Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

The ongoing revolution in industrial production- Industry 4.0, is driven by transformative technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), single-board computers, and 5G communication. As the trend towards IIoT continues, an increasing number of industrial drive systems and their fleets are being connected to the cloud. This enables the manufacturers to perform condition monitoring (CM) and streamlined maintenance activities. At the heart of these drive systems are Power Electronics Systems (PESs), which operate at high switching frequencies (10 kHz–1 MHz) to efficiently transfer electrical power and deliver it to a load in a controlled manner. However, due to their functionalities and the presence of semiconductor switches, PESs are susceptible to failure, necessitating effective condition monitoring (CM) for fault detection and improved lifetime. Link to this issue, to enable CM based on high-frequency data, an industrial site with multiple electric drives is required to record data up to 15TB/week. Therefore, there is a demand from industrial partners to establish intelligent communication between a fleet of physical systems and the cloud to reduce transmission, storage, and bandwidth costs, as well as to enable real-time fault detection and learning from fleet operations. This paper proposes an intelligent edge–cloud computing methodology to address the challenge of high-frequency data monitoring for PESs, focusing on novelty detection and selective data transmission to reduce transmission costs. The methodology involves developing a novel edge–cloud framework that incorporates a neural network-based novelty detector for selective data transmission from physical systems to the cloud. The proposed methodology is evaluated through hardware tests, demonstrating a significant reduction in data transmission (94%) and potential cost savings of up to €5.9k/year for a single remote system. 95.6% detection accuracy of the PQ phase is obtained during experimental tests over 590 samples. Thus, this paper contributes to the vision of the smart grid and IIoT by analyzing the Power Quality (PQ) monitoring problem of a three-phase grid and showcasing the capability of the proposed framework in terms of novelty detection and data transmission cost reduction. To conclude, the proposed intelligent edge–cloud computing methodology offers a promising solution for effective condition monitoring of PESs, with potential cost savings and improved fault detection capabilities. By leveraging advanced technologies and intelligent data-driven approaches, this framework advances the goals of Industry 4.0 and paves the way for efficient and reliable industrial operations in the digital age.

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work. To solve it, a joint reparameterization feature pyramid network (JRFPN) is proposed. Firstly, three different reparameterization tricks are designed to reform temporal information and exchange cross-temporal information, to alleviate the damage of long-term dependency. Secondly, a joint condition monitoring framework is designed, aiming to suppress feature confounding between poor-quality data and faulty data. The auxiliary task is trained to extract the degradation trend. The main task fights against feature confounding and dynamically delineates the failure threshold. The degradation trend and failure threshold decisions are corrected for each other to make the final joint state inference. Besides, considering the different quality of the monitoring variables, a channel weighting mechanism is designed to strengthen the ability of JRFPN. The measured data proved that JRFPN is more effective than other methods.

Enhancing internal combustion engine fault detection through co-simulation with piezoelectric pressure measuring chains

This study undertakes a proof-of-concept investigation,employing a combination of physics-based and empirical models toanalyze an in-cylinder pressure piezo-electric measurement chain. Theprimary objective is to systematically characterize and comprehenddeviations arising from the components within the measuring chain. Themethod implemented elucidates the real-time capabilities of the measuringchain model, thereby refining raw measurement data to discern deviations(errors) in the measuring chain components. This endeavor is gearedtowards facilitating effective condition monitoring and enhancing systemreliability. We detail our approach, which integrates co-simulationtechniques with piezo-electric pressure measuring chains, to detect andmitigate faults within internal combustion engines. Through this approach,we develop a comprehensive understanding of the underlying dynamics ofthe measuring chain, allowing for automated parameter estimation usinggenetic algorithms. Our key findings reveal significant improvements infault detection accuracy, with a notable reduction in in-cylinder pressureerrors. This study not only contributes to advancing fault detectionmethodologies but also holds promise for optimizing engine performanceand reliability in various applications.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Physics-Based Modelling for On-Line Condition Monitoring of a Marine Engine System

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100–150 °C to 70–80 °C and the head vibration features decreased from the confidence interval 900–1300 m/s2 to around 200–300 m/s2. For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework.

Comparative Study on Health Monitoring of a Marine Engine Using Multivariate Physics-Based Models and Unsupervised Data-Driven Models

The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines.

Evaluation of Component Level Degradation in the Boeing 737-800 Air Cycle Machine

Abstract An aircraft is composed of several highly integrated and complex systems that enable it to deliver safe and comfortable flight. Its functionality is therefore strongly dependent on the safe operation of these systems within their designed optimal efficiencies. The air cycle machine (ACM) is a subsystem of the pressurized air conditioner (PACK) system, its key function is to enable refrigeration of the air in order to comply with the wide range of cabin environment requirements for maintaining aircraft safety and passenger comfort. The operation of the ACM is governed by the PACK control system which can mask degradation in its component during operation until severe degradation or failure results. The required maintenance is then both costly and disruptive. The ACM has been reported as one of the most frequently replaced subsystem and has been therefore reported as a major driver of unscheduled maintenance by the operators. This paper aims to investigate the component level degradation in the ACM at various severities and quantify the impact of its performance characteristics and associated interdependencies at PACK system level. In this paper, Cranfield University’s in-house environmental control system (ECS) simulation framework called simscape ECS simulation under all conditions (SESAC) has been implemented to evaluate degradation in the ACM components in a representative Boeing 737-800 aircraft PACK model. The fault modes of interest are those highlighted by the operators and correspond to the ACM compressor, turbine, and interconnecting mechanical shaft efficiency degradation. Simulation results, in terms of temperature, pressure, and mass flow at various degradation severities, are presented and discussed for each component at PACK system level. The acquired results suggest that, for all three fault modes, the PACK controller can compensate for an ACM degradation severity of up to 20%, allowing the PACK to sustain the delivery of the demanded temperature and mass flow. For degradation severity of above 20%, the PACK is able to deliver the demanded temperature with a substantially reduced mass flow. This has a significant impact on the PACK’s ability to meet the cabin demand efficiently. The methodology reported and the findings conceived to serve as an enabler toward formulating an effective PACK fault diagnostics and condition monitoring solution at system level, and fault reasoning at vehicle level.