A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation

Diagnosing bearing faults is crucial for maintaining, ensuring reliability, and extending the lifespan of rotary machines. This process helps prevent unexpected downtime in industries, ultimately reducing economic losses caused by the failure of rotary machines. Timely diagnosis of bearing faults is crucial to prevent catastrophic breakdowns, minimize maintenance expenses, and ensure uninterrupted productivity. With industries evolving rapidly and machines operating in increasingly diverse conditions, traditional fault detection methods face limitations. Despite extensive research in recent decades, there is an ongoing need for further advancements to enhance existing fault diagnosis techniques. This study addresses these challenges by utilizing advanced machine learning algorithms Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit Network (GRU), Bidirectional LSTM, and for precise bearing fault diagnosis. Leveraging the CWRU dataset encompassing diverse fault classes and machine conditions, a comprehensive data preprocessing pipeline was executed to clean, normalize, and augment the dataset, ensuring model readiness and enhancing performance. Performance analysis revealed the proposed models achieving remarkable accuracies on the CWRU dataset. The CNN and LSTM models attained accuracies of 95%, while the RNN and GRU models achieved accuracies of $97\%$ . Additionally, the Bidirectional LSTM model yielded an accuracy of 96%. These results signify substantial advancements in bearing fault diagnosis, emphasizing the models’ efficacy in accurately detecting and categorizing faults within the 10 classes of the CWRU dataset. The findings underscore the potential of advanced machine learning techniques in revolutionizing fault diagnosis for rotary machines, addressing the persistent need for more robust and accurate diagnostic methodologies

Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these units to construct a network. Unsupervised learning is also carried out for each restricted Boltzmann machine layer in this procedure to compute the network weights and biases. Finally, the network output is fed into SFAM to perform fault classification. In order to diagnose the valve faults, three signal types of vibration, pressure, and current are acquired from a two-stage reciprocating air compressor under different valve conditions such as suction leakages, discharge leakages, spring deterioration, and their combination. These signals are subsequently processed so that the useful fault information from the signals can be revealed; next, statistical features in the time and frequency domains are extracted from the signals and used as the inputs for hybrid deep belief network. Performance of hybrid deep belief network in fault classification is compared with that of the original deep belief network and the deep belief network combined with generalized discriminant analysis, where softmax regression is used as a classifier for the latter two models. The results indicate that hybrid deep belief network is more capable of improving the diagnosis accuracy and is feasible in industrial applications

Towards Controlled Transmission: A Novel Power-Based Sparsity-Aware and Energy-Efficient Clustering for Underwater Sensor Networks in Marine Transport Safety

Energy-efficient management and highly reliable communication and transmission mechanisms are major issues in Underwater Wireless Sensor Networks (UWSN) due to the limited battery power of UWSN nodes within an harsh underwater environment. In this paper, we integrate the three main techniques that have been used for managing Transmission Power-based Sparsity-conscious Energy-Efficient Clustering (CTP-SEEC) in UWSNs. These incorporate the adaptive power control mechanism that converts to a suitable Transmission Power Level (TPL), and deploys collaboration mobile sinks or Autonomous Underwater Vehicles (AUVs) to gather information locally to achieve energy and data management efficiency (Security) in the WSN. The proposed protocol is rigorously evaluated through extensive simulations and is validated by comparing it with state-of-the-art UWSN protocols. The simulation results are based on the static environmental condition, which shows that the proposed protocol performs well in terms of network lifetime, packet delivery, and throughput

Numerical sensitivity analysis of temperature-dependent reaction rate constants for optimized thermal conversion of high-density plastic waste into combustible fuels

This is the peer reviewed version of the following article: Irfan M, Un Nabi RA, Hussain H, Naz Y, Shukrullah S, Khawaja HA, Rahman S, Althobiani F. Numerical sensitivity analysis of temperature-dependent reaction rate constants for optimized thermal conversion of high-density plastic waste into combustible fuels. Canadian Journal of Chemical Engineering. 2023, which has been published in final form at https://doi.org/10.1002/cjce.2488310. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.The use of experimental rate constants for producing a high yield of liquid fuels from the pyrolysis of plastic waste is not widely accepted owing to a lack of compatibility between the different kinetic rate constants responsible for successful conversion reactions. In R software, the Arrhenius law can forecast the ideal combination of reaction rate constants and frequency factors and then perform sensitivity analysis on individual rate constants to estimate the selectivity and quantity of primary pyrolysis products. Sensitivity analysis is a way of determining the effectiveness of individual rate constants in the reaction. This research element is currently lacking in the literature for the cost-effective valorization of plastics into combustible fuels. We are the first to use R software to perform sensitivity analysis on specific rate constants by reducing or raising their initial values to a point where maximum oil yield is attainable in the temperature range of 340°C to 370°C. The primary focus was to save time and cost of extracting empirical rate constants from experiments to produce commercial-scale pyrolytic oil. The H-abstraction, chain fission, polymerization, and scission reactions were chosen due to the high availability of free radicals for maximum oil production. The oil recovery rate improved drastically to 90% at the end of processing time, while the number of byproducts gradually decreased. The k8 rate constant driven reaction is the best-suited condition for industrial-scale pyrolysis of high-density plastics into liquid fuels, with 74% improvement in oil production and 14% improvement in light wax during sensitivity analysis

Mitigation of Phase Noise and Nonlinearities for High Capacity Radio-over-Fiber Links

Radio-over-fiber (RoF) links successfully provide high data rates and bandwidth capacity with a low complexity system architecture, as compared to its counterpart digital-RoF. In addition, the compound of quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexed (OFDM) modulation schemes further enhance the process of these achievements. However, high data rates and bandwidth-capacity-supported RoF links face nonlinearities (NLs), linear distortions (LDs), and phase noise challenges that degrade the reliability of communication networks (CNs). Therefore, in this paper, to suppress NLs, LDs, and phase noise, next generation cloud radio access networks (CRANs) are investigated using RoF links and wavelength division multiplexing (WDM) methodology based on 16, 32, and 64 QAM-OFDM modulation schemes. The receiver of the proposed framework is designed, applying an improved digital signal processing (DSP) system that includes overlap frequency domain equalization (OFDE), a synchronization process, and time domain equalization (TDE). Theoretical and simulation models are organized for estimating the proposed RoF link with the aid of different values of transmission ranges, input power, output power, bit rate, bits per symbol, channel spacing, and the number of users. The fitness of the model matches that of existing approaches

Design and Experimental Analysis of Multiband Frequency Reconfigurable Antenna for 5G and Sub-6 GHz Wireless Communication

A low-profile frequency reconfigurable monopole antenna operating in the microwave frequency band is presented in this paper. The proposed structure is printed on Flame Retardant-4 (FR-4) substrate having relative permittivity of 4.3 and tangent loss of 0.025. Four pin diode switches are inserted between radiating patches for switching the various operating modes of an antenna. The proposed antenna operates in five modes, covering nine different bands by operating at single bands of 5 and 3.5 GHz in Mode 1 and Mode 2, dual bands (i.e., 2.6 and 6.5 GHz, 2.1 and 5.6 GHz) in Mode 3 and 4 and triple bands in Mode 5 (i.e., 1.8, 4.8, and 6.4 GHz). The Voltage Standing Waves Ratio (VSWR) of the presented antenna is less than 1.5 for all the operating bands. The efficiency of the designed antenna is 84 % and gain ranges from 1.2 to 3.6 dBi, respectively, at corresponding resonant frequencies. The achieve bandwidths at respective frequencies ranges from 10.5 to 28%. The proposed structure is modeled in Computer Simulation Technology microwave studio (CST MWS) and the simulated results are experimentally validated. Due to its reasonably small size and support for multiple wireless standards, the proposed antenna can be used in modern handheld fifth generation (5G) devices as well as Internet of Things (IoT) enabled systems in smart cities

An Application to Transient Current Signal based Induction Motor Fault Diagnosis of Fourier-Bessel Expansion and Simplified Fuzzy ARTMAP

The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier-Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy

Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods

GIS and Remote Sensing-Based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones: A Case Study for Industrial Zones in Bangladesh

Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km2) of the area is very low, 3.83% (63.18 km2) of the area is low, 56.2% (927.05 km2) of the area is medium, 39.25% (647.46 km2) of the area is high, and the rest 0.72% (11.82 km2) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management