State Of Machine Research Articles

Modelling the Reliability of Logistics Flows in a Complex Production System

This paper analyses the disruptions occurring in a production system determining the operating states of a single machine. A system with a convergent production character, in which both single flows (streams) and multi-stream flows occur, was considered. In this paper, a two-level formalisation of the production system (PS) was made according to complex systems theory. The continuity analysis was performed at the operational level (manufacturing machine level). The definition of the kth survival value and the quasi-coherence property defined on chains of synchronous relations were used to determine the impact of interruption of the processed material flow on uninterrupted machine operation. The developed methodology is presented in terms of shaping the energy efficiency of technical objects with the highest power demand (the furnace of an automatic paint shop and the furnace of a glass tempering line were taken into consideration). The proposed methodology is used to optimise energy consumption in complex production structures. The model presented is utilitarian in nature—it can be applied to any technical system where there is randomness of task execution times and randomness of unplanned events. This paper considers the case in which two mutually independent random variables determining the duration of correct operation TP and the duration of breakdown TB are determined by a given distribution: Gaussian and Gamma family distributions (including combinations of exponential and Erlang distributions). A formalised methodology is also developed to determine the stability of system operation, as well as to assess the potential risk for arbitrary system evaluation parameters.

Dynamic Model-Embedded Intelligent Machine Fault Diagnosis Without Fault Data

Intelligent machine fault diagnosis technique has recently exploded interest in digital health, energy power, and industrial maintenance. Collecting machine fault data in engineering practice is usually costly, causing big challenges in the intelligent diagnosis of most fresh-from-the-factory machines that are missing fault data. Inspired by the main idea of the digital twin, this paper proposes a dynamic model-embedded intelligent machine fault diagnosis framework. Specifically, a machine dynamic modeling and parameter identification approach is introduced for building the machine digital model. The digital model is used to predict machine fault data from the measured healthy vibration signals. Furthermore, a parameterized convolutional neural network structure is designed for learning optimization features and recognizing the machine's healthy state. Experimental investigation demonstrates the effectiveness of the framework. The results show that the proposed framework enables intelligent machine fault diagnosis without fault data. The diagnosis effect outperforms supervised, unsupervised and small-sample learning approaches and can be generalized to another load and speed with acceptable accuracy.

Machine fault diagnosis based on data reassembly for a deep neural network trained by imbalanced acoustic recordings

For high-tech equipment, online monitoring and diagnostics play a critical role in ensuring production under normal conditions. While the machines are generally in normal condition, fault conditions are often difficult, if not impossible, to simulate. In this paper, we propose a data augmentation approach to address the challenge of machine fault diagnosis for the above-mentioned scenarios where the normal and abnormal acoustic data sets are imbalanced. To overcome this limitation, we propose a data reassembly approach to generate new signal samples by randomly combining data segments from different machine states. The resulting sample is annotated as a vector according to the proportion of the corresponding machine state. The training with data reassembly is performed for a deep neural network (DNN)-based classifier using the log Mel spectrum as the acoustic features. To validate the proposed approach, we conducted experiments using a rotor kit. Acoustic data associated with the normal and seven abnormal conditions were recorded using a four-microphone uniform circular array (UCA). The results showed that the classifier with data reassembly had superior diagnostic performance compared to the classifier without data reassembly. The proposed method is also compared to several data generation baselines in terms of accuracy and F1 score.

EVALUATING THE MANAGEMENT AND MAINTENANCE PRACTICES OF LINEAR ACCELERATOR AND COBALT-60 CANCER TREATMENT MACHINES IN NIGERIA: A COMPREHENSIVE ASSESSMENT

This research paper investigated the state of radiotherapy machines and their management in cancer treatment centers in Nigeria. The study aimed to identify the challenges faced by these centers and propose strategies for improvement. Through a comprehensive analysis of data collected from surveys and questionnaires, critical issues impacting the availability, reliability, and effectiveness of radiotherapy machines were highlighted. The findings revealed a significant shortage of radiotherapy machines across the nation, with Nigeria having less than 6% of the required number of linacs. This scarcity led to a high workload on the existing machines, resulting in long waiting lists and compromised treatment outcomes. Management challenges, such as the absence of preventive maintenance policies, inadequate personnel training, delayed spare parts delivery, and limited engagement of maintenance contractors, were identified. The research emphasized the need for supportive cancer treatment machines, including orthovoltage and superficial machines, to alleviate the burden on existing radiotherapy machines. However, most centers lacked these supportive machines, limiting their ability to provide comprehensive care to patients. Financial sustainability also emerged as a significant concern, as internal funds proved insufficient for maintenance and operations. The involvement of the government and the establishment of independent regulatory bodies were proposed to address these challenges. In conclusion, this study called for urgent action to address the gaps and challenges in radiotherapy machine management in Nigeria. It highlighted the importance of increasing the number of machines, implementing preventive maintenance policies, enhancing staff training, and securing adequate funding. By doing so, the quality of cancer care could be significantly improved, leading to better patient outcomes and a more robust healthcare system in Nigeria.

Modeling degradation of the actual state of rolling bearings based on a unified diagnostic criterion

Background. Without any claims for comprehensive coverage, this article presents some results of modeling technical degradation of the rolling bearings used in the power and mechanical components of mining machines. The results of comprehensive diagnostics of mining equipment based on vibration parameters made it possible to propose a new approach to modeling the processes of changing the actual state of mining machines. Objective. The research aims at performing mid-term forecasting of defect development in the rolling bearings installed in the power and mechanical components of mining machines as well as at demonstrating the efficiency of the new unified diagnostic criterion when it is used as a simulated parameter in an adaptive mathematical model of degradation. Research methods. Results of a complex diagnostic approach to analyzing vibration parameters were applied in the this research, including the spectral analysis in the extended frequency and dynamic range, spectrum envelope and kurtosis analysis. The results obtained confirm the efficiency of the proposed set of diagnostic features and defect detection rules in analyzing mechanical vibration parameters as applied to the task of creating a unified diagnostic criterion for assessing the technical condition of rolling bearings.

Aligning process quality and efficiency in agricultural soil tillage

Abstract Automation in agricultural machinery is a crucial driver of productivity and sustainability. Some automation features like automated steering and real-time data analytics are already state-of-the-art. On the other hand, a human driver performs the optimization of the working speed manually, and the automation of this is an ongoing challenge. Process quality and process efficiency are the two main targets in this optimization. Agricultural soil tillage requires achieving both. Therefore, the correlation between process quality optimization and process efficiency is fundamental, and vice versa. The approach presented in this paper shows how the two optimization targets of efficiency and process quality can be optimized and aligned together. Optical sensors determine various parameters to describe and model the process quality. The measured machine state determines the characteristics of the interaction forces between the machine and the environment. A machine learning algorithm describes the relationships in the drivetrain. The two process targets are each predicted for different working speeds and are combined in the form of a boundary target and an optimization target to identify one optimized target speed value.

Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring

This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.
 The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.
 However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.
 There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.
 The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.
 By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.
 Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.
 This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.

PD-PAn: Prefix- and Distribution-Preserving Internet of Things Traffic Anonymization

One of the features of network traffic in Internet of Things (IoT) environments is that various IoT devices periodically communicate with their vendor services by sending and receiving packets with unique characteristics through private protocols. This paper investigates semantic attacks in IoT environments. An IoT semantic attack is active, covert, and more dangerous in comparison with traditional semantic attacks. A compromised IoT server actively establishes and maintains a communication channel with its device, and covertly injects fingerprints into the communicated packets. Most importantly, this server not only de-anonymizes other IPs, but also infers the machine states of other devices (IPs). Traditional traffic anonymization techniques, e.g., Crypto-PAn and Multi-View, either cannot ensure data utility or is vulnerable to semantic attacks. To address this problem, this paper proposes a prefix- and distribution-preserving traffic anonymization method named PD-PAn, which generates multiple anonymized views of the original traffic log to defend against semantic attacks. The prefix relationship is preserved in the real view to ensure data utility, while the IP distribution characteristic is preserved in all the views to ensure privacy. Intensive experiments verify the vulnerability of the state-of-the-art techniques and effectiveness of PD-PAn.

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

VMIFresh: Efficient and fresh caches for virtual machine introspection

Virtual machine introspection (VMI) is the process of extracting knowledge about the inner state of a virtual machine from the outside. Traditional passive introspection mechanisms have proved themselves ineffective in many application domains due to their low performance. As a remedy for this issue, caching at the level of the introspection application was introduced. However, this sacrificed the freshness of VMI and led to an inconsistent outside view.In this work, we propose a multi-purpose hybrid caching scheme with freshness and consistency guarantees that is interleaved with the guest's MMU. This scheme can easily be integrated into existing applications and frameworks such as libvmi and Volatility 3. We demonstrate its feasibility by developing a prototype for such applications. Furthermore, the experimental evaluation of our approach suggests that it even significantly exceeds the performance of previous inconsistent caches.

The Evaluation of the Technical Condition of Parts and Assemblies of Mining Equipment Based on the Analysis of a Sample of Working Fluids

The article is informative and is devoted to the description of methods for research samples of hydraulic and lubricating fluids of technological equipment at the gold mining enterprise JSC "AK Altynalmas". The advantages of methods for monitoring the working state of the mining machine are given. The analysis of the results of laboratory studies of the working fluids of the CAT 992K loader was carried out.

An adaptive VNCMD and its application for fault diagnosis of industrial sewing machines

Due to the non-linearity and non-stationarity of sound signals from complex transmission structure and frequent start-stop of industrial equipment, such as sewing machine, its incipient faults cannot be accurately identified with classical fault diagnosis methods. Therefore, a new fault diagnosis method with an adaptive variational nonlinear chirp mode decomposition (AVNCMD) is proposed. Firstly, a pre-processing step of convex optimization with generalized minimax-concave penalty function and spline-kernelled chirplet transform is applied to obtain the time–frequency distribution (TFD) with energy concentration, and a peak frequency extension method is proposed to adaptively search initial instantaneous frequencies of AVNCMD on the TFD; then the Pearson correlation coefficient is introduced as the termination condition of the adaptive signal decomposition. Secondly, multi-domain features are extracted from the decomposed nonlinear chirp modes, and an optimal features set is constructed by the feature preference method of an improved ReliefF. Finally, the fault classification is realized by the extreme learning machine, whose input weights and biases are optimized via the salp swarm algorithm. The AVNCMD is testified to be accurate in simulation with nonlinear amplitude modulation–frequency modulation signals and be effective in actual sound signals of industrial sewing machines on four different states. Through the detailed analysis of the measured sound signals in factory, the proposed fault diagnosis method can identify four states of the industrial sewing machines with an overall identification accuracy of 95.0%.

Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems

The textile and apparel industry is one of the largest industries in the world, with significant environmental and social impacts, such as manual labor in poor working conditions and resource depletion and environmental degradation due to ineffective production. As a result, there is an increasing need for sustainable practices and a transition toward a more circular and responsible industry. In this paper, we introduce the proposed approach in the context of a sustainable transition for the industrial textiles. Instead of human labor, the automated solution to detect defects is proposed for the 4.0 revolution in general and the textile industry in particular. Specifically, the mechanical design of a three-dimensional model for small- and medium-sized fabric rolls is portrayed. Some computational mechanics are created to ensure a stable mechanism and proper actuators. For acquiring accurate data and measuring the machine state, several installations, that is, a digital camera, load cell, and positioning sensor, and the layout of the light source are improved to intensify the image quality. The control theme, such as the cross-coupling controller, offers superior performance in synchronization and vibration-less motion. Lastly, the excellent detection method is provided by the convolutional neural network scheme for offline training to distinguish defects on products. Instead of a manual check or low rate of defect detection on products, the contributions of this research are as follows: (a) analyzing the mechanical design of the textile machining system in terms of dynamic characteristics; (b) implementing a smooth scheme based on motion control synchronization; and (c) integrating various strategies, including vision-based computation and control topology, to demonstrate superior performance. These results highlight that our approach can be widely adapted to address sustainability challenges not only in the textile field but also in global manufacturing practices.

Reinforcement learning for energy-efficient control of parallel and identical machines

Nowadays, the growing interest in industry for enhancing the sustainability of manufacturing processes is becoming a major trend. Energy consumption can be lowered by controlling machine states with energy-efficient control policies that switch off/on the device. Recent studies have shown that Reinforcement Learning algorithms can effectively control manufacturing systems without the requirement of prior knowledge about system parameters. This is a significant factor since full information on system dynamics is difficult to obtain in real-world applications. This work proposes a new Reinforcement Learning-based algorithm to apply energy-efficient control strategies to a single workstation consisting of identical parallel machines. The model goal is to achieve the optimum trade-off between system productivity and energy demand without relying on full knowledge of the system dynamics. Numerical experiments confirm effectiveness, applicability, and generality of the proposed approach, even when applied to a real-world industrial system from the automotive sector.

П’яти координатні верстати з ЧПК: процеси обробки та проблеми функціонування верстатів з ЧПК

The current state of machines with CNC (computer numerical control) and the problems that arise when using them are considered. The existing solutions to problems are described, and a new way of protecting machine components from breakdowns is proposed. Protection is based on the use of piezoelectric force and acceleration sensors. Additional protection can be imple-mented with the help of software in the CNC device.

A new multi-sensor fusion with hybrid Convolutional Neural Network with Wiener model for remaining useful life estimation

With the development of smart manufacturing, the health monitoring of the machines has become important. Remaining useful life (RUL) estimation, which could predict the future machine state, has attracted much more attentions. Deep learning (DL) based RUL has achieved remarkable results. But it still faces the issues on the multi-sensor fusion process and the health index (HI) construction, and both of these two issues can affect DL models for RUL. To overcome these issues, this research designed a new hybrid model of Convolutional Neural Network (CNN) and Wiener process, named hybrid CNN-Wiener model. First, the CNN network is adopted to achieve feature-level fusion of multi-sensor signals and to calculate the virtual HI of the machine. Second, the Wiener process model is developed to estimate the value of RUL using virtual HI. Third, the Wiener process model is designed as the layer in CNN network and trained with CNN together. The hybrid CNN-Wiener model has been tested on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, and its results show that the hybrid CNN-Wiener model has obtained the remarkable promotion by comparing with other famous DL models. The ablation studies have been tested and it shows that the hybrid CNN-Wiener model has been promotion largely with the Wiener model and the multi-sensor fusion techniques.

Adaptive fusion based on physics-constrained dictionary learning for fault diagnosis of rotating machinery

Modern manufacturing systems rely on multiple types of sensors to monitor the manufacturing processes and machine faults to ensure product quality. The costs associated with sensor installation, maintenance, data transmission, and data storage are high. In this paper, a new data fusion method based on physics-constrained dictionary learning is proposed to improve the efficiency of data collection and the accuracy of fault diagnosis. In the proposed dictionary learning method, the measurement, basis, and classification matrices are optimized for the effective use of compressed sensing technique in process monitoring. With the optimized matrices, full-scale high-resolution signals can be reconstructed from a small number of sensor measurements. At the same time, machine states can be identified based on the sparse measurements. An adaptive weight scheme is introduced to combine low-cost sensor data so that the accuracy of fault diagnosis can be improved. The proposed method is tested with an experimental dataset of gearbox vibrations caused by gear cracks and different levels of crack severities are classified. The results show that up to 70% of data collection can be saved with the new approach while more than 95% of diagnosis accuracy is achieved. The sensitivities of the performance with respect to the number of measurements and number of sensors are also studied.

Energy efficiency improvement of industrial parts washers using state control

In recent years, the energy efficiency topic drastically increased its relevance in manufacturing industry management. One of the most supported strategies to reduce the energy consumed by manufacturing equipment is the machine state control during idle periods, triggering the machine in a standby state with low power request. This approach is aimed at reducing system energy consumption while not jeopardizing the overall production rate and it is referred to as energy efficient control (EEC). Policies implementing EEC techniques are proven to be effective in a manufacturing system but have been tested only for assembly and machining operations. This work is focused on industrial parts washers: widely used machines in manufacturing with significant energy consumption associated. The objective is to demonstrate the applicability and the potential of EEC strategies when applied to washing processes. Proper EEC policies are identified for an industrial parts washer operating in a real production line in the automotive sector. Different scenarios are analyzed and the focus is placed on the effect that these energy efficient actions have on the overall production system in terms of throughput and energy consumption. In this way, the industrial impact of the EEC application on the industrial parts washer is computed by running simulation experiments.

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network.

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.

Deep learning for automated fish grading

Fish is a staple food around the globe, and its quality is heavily dependent on freshness. The conventional method for evaluating the quality of fish is through a visual inspection of a sample. However, this approach heavily depends on human senses for precise assessment, leading to a susceptibility to variability in accuracy and efficiency. Moreover, the potential for safety to be compromised due to errors in the evaluation process makes it a less reliable method. This work presents two Neural Network (NN) architectures, FishNET-S and FishNET-T, to evaluate the quality of the Indian Sardinella and the Yellowfin Tuna, respectively, using RGB images captured from smartphone cameras. The FishNET-S is based on the VGG-16 with the introduction of a Block Attention Module (BAM) to drive the network towards learning the features related to fish quality evaluation from the eye region of the entire fish. In contrast, the FishNET-T architecture first performs a color decomposition based on hue, saturation, and intensity transformations before forwarding the hue and saturation components to the CNN in order to effectively identify grades through fish meat. Experimentally, FishNET-S has managed to obtain an accuracy of 84.1%, while FishNET-T yielded an accuracy of 68.3%. The comparison analysis carried out with the use of generic machine learning and state of the art deep learning models shows that the performance of the proposed novel architectures is dominant and unchallenged.