Machine Condition Research Articles

DIAGNOSING FAULTS IN THE TIMING SYSTEM OF A PASSENGER CAR SPARK IGNITION ENGINE USING THE BAYES CLASSIFIER AND ENTROPY OF VIBRATION SIGNALS

Today's systems for diagnosing the technical condition of machines, including vehicles, use very advanced methods of acquiring and processing input data. Presently, work is being conducted globally to solve related problems. At the moment, it is not yet possible to create a single procedure that would enable the construction of a properly functioning diagnostic system, regardless of the selected object to be diagnosed. Hence, there is a need to conduct further research into the possibility of using already developed methods, as well as their modification to other diagnostic cases. This article presents the results of research related to the use of the Bayes classifier for diagnosing the technical condition of passenger car engine components. Damage to the exhaust valve of a spark ignition engine was diagnosed. The source of information on the technical condition was vibration signals recorded at various measuring points and under different operating conditions of the car. To describe the nature of changes in the vibration signals, the entropy measures were determined for the decomposed signal using the discrete wavelet transform is proposed.

Risk Evaluation Concept of Engine Room Module Installation on Modular Construction Shipbuilding

The implementation of the construction of modular construction ships is able to accelerate ship production by up to 50%. However, in practice, delays in the production process can still be found in the engine room module of the modular ship building process. Production delays occur due to inappropriate block dimension accuracy, damaged machine conditions, loading blocks that are not on schedule, many revisions to drawings, delays in equipment and basic ship materials. The production process includes a series of activities carried out including: Hull construction, Painting & Protection, Outfitting Manufacture, Machinery Outfitting, Accommodation Outfitting, Electric, Electrical Outfitting. This research aims to evaluate the performance of engine room module installations in shipyards that apply a modular construction system. At an early stage, the research was conducted by identifying factors and constraints that affect the performance of the engine room installation in shipbuilding which has the potential to cause delays. The conceptual and technical approach are carried out by analyzing the causal relationship of the identification of factors at the production stage. The results of this research is expected to anticipate earlier delays in the engine room module installation with the concept of evaluation using Bayesian Network integration and System Dynamics.

Methods and means of process risk mitigation during operation of equipment for applica-tion of chemical means of combined potato-setting machines

Ensuring safe and healthy working conditions in biotechnical systems associated with the implementation of technological processes for the production of crop products is a complex system problem, the solution of which requires not only an assessment of the parameters of the technical condition of technological machines, but also the correctness of the certain actions of the human operator of the machine-tractor unit, as well as the properties of production items and sanitary and hygienic working conditions. One of the effective methods of technogenic nature in solving the problem related to minimization of technological risks leading to chemical contamination of the agricultural environment during the operation of the investigated complex is to equip it with digital automated devices for active control of the quality of dosing of introduced materials, which would promptly provide real-time forecasting of the state of the investigated object and carry out information-measuring and restoration functions. The creation of automated active monitoring devices required special experimental studies under normal operation conditions with obtaining a large amount of data and their statistical processing, the results of which formed the basis of this development.

Recent Progress on Triboelectric Nanogenerators for Vibration Energy Harvesting and Vibration Sensing.

The triboelectric nanogenerator (TENG) is a recent technology that reforms kinetic energy generation and motion sensing. A TENG comes with variety of structures and mechanisms that make it suitable for wide range of applications and working conditions. Since mechanical vibrations are abundant source of energy in the surrounding environment, the development of a TENG for vibration energy harvesting and vibration measurements has attracted a huge attention and great research interest through the past two decades. Due to the high output voltage and high-power density of a TENG, it can be used as a sustainable power supply for small electronics, smart devices, and wireless sensors. In addition, it can work as a vibration sensor with high sensitivity. This article reviews the recent progress in the development of a TENG for vibration energy harvesting and vibration measurements. Systems of only a TENG or a hybrid TENG with other transduction technologies, such as piezoelectric and electromagnetic, can be utilized for vibrations scavenging. Vibration measurement can be done by measuring either vibration displacement or vibration acceleration. Each can provide full information about the vibration amplitude and frequency. Some TENG vibration-sensing architectures may also be used for energy harvesting due to their large output power. Numerous applications can rely on TENG vibration sensors such as machine condition monitoring, structure health monitoring, and the Internet of things (IoT).

Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks

The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the framework of industrial applications are not established yet. In this contribution, a procedure is presented for the development of data-driven condition monitoring solutions for industrial hydraulics using supervised learning and neural networks. The proposed method involves feature extraction as well as feature selection and is applied on simulated data of a hydraulic press. Different steps of the development process are investigated regarding the design options and their efficacy in fault classification tasks. High classification accuracies could be achieved with the presented approach, whereas different faults are shown to require different configurations of the classification models.

An Oil Wear Particles Inline Optical Sensor Based on Motion Characteristics for Rotating Machines Condition Monitoring

Since inline monitoring method has the advantages of no sampling, being real-time, no human intervention, and low error, this paper innovatively proposes to study the inline monitoring of wear particles in an oil pipeline, from the perspective of the different motion characteristics of the particles. In this paper, an inline optical sensor was designed and developed by studying the velocity characteristics of different particles through theoretical calculations, numerical simulations, and experimental analysis. First, an equation for particle motion was statistically established, based on the forces acting on wear particles in an oil-filled vertical tube. Then a finite element model of particle motion in a full-flow oil pipeline was created, to simulate particle motion with various diameters, densities, locations, and shapes. Finally, the results of the theoretical study were effectively applied to design an inline optical monitoring sensor, and the experimental validation results demonstrated that the inline sensor has excellent suitability for monitoring wear particles. This study has significance for the safe operation of large rotating machinery.

A revisit for the diagnosis of the hollow ball screw conditions based classification using deep learning

Hollow ball screws play a vital role in high-quality precision manufacturing, in which sensors are used to obtain useful data. The use of artificial intelligence to determine the condition of machines is a major trend, and installing multiple sensors on relevant objects may be prohibitively expensive. This study compares a machine learning method based on a support vector machine (SVM) with deep learning methods. In the machine learning strategy, built-in signals from internal parameters are used to determine the condition of the hollow ball screw. Rather than using a graphics processing unit (GPU), feature engineering and then Fisher’s score were applied to determine the most representative parameters from fusion sensor signals to perform SVM classification. For the deep learning method, a GPU was employed instead of data cleaning; a long- and short-term memory (LSTM) network and hybrid convolutional neural network (CNN) and an LSTM network were used to automatically extract effective features and then perform classification. Thus, the deep learning approach enabled classification of one-dimensional (1D) and multidimensional data sets, through which users can obtain suitable configurations for various sensors according to cost and other requirements for classification accuracy. We compared the accuracies of the LSTM, CNN–LSTM, and SVM models by using multiple datasets. The 3D LSTM model with current, rpm, and additional linear scale signals provided good synergy for feature extraction and resulted in the maximum accuracy close to the highest accuracy of the supervised fine-tuned SVM model by using current signal. Datasets that are unrepresentative according to FE analysis may be useful in deep learning. The advantages of using a deep learning model without feature extraction relative to those of SVM learning was investigated.

규칙기반진단 자동화를 위한 Signal Recognition 기술개발

Rule based diagnosis is a machine condition diagnostic technology, and the result obtain through its responses to attributes consists of expert knowledge and experience. Accordingly, unlike machine learning, data and general-purpose aspects have advantages as they do not require big data and learning. However, rule-based diagnosis requires the user to respond to attributes, resulting in individual errors or time costs. Hence, it needs to be performed automatically. This paper develops a signal recognition technique by analyzing the diagnostic parameters of the rule-based diagnostic attributes. The diagnostic parameter consists of two characteristics and is recognized using different techniques. It is based on signal recognition, confirming its diagnostic potential, Further, this study is expected to enhance the automation of this diagnosis.

Framework for stochastic modelling of long-term non-homogeneous data with non-Gaussian characteristics for machine condition prognosis

To make prognosis one needs to build a model based on historical data. In the paper we propose a framework for modelling of long-term non-homogeneous data with non-Gaussian properties. These specific properties have been identified in real datasets describing the degradation process of the machine. The framework covers deterministic and random components separation, modelling of heavy-tailed, time-varying properties of random part as well as identification of possible autodependence hidden in the random sequence and identification of distribution for a random part. Due to non-linear trends, time-dependent scale (equivalent to the variance for Gaussian distributed data) and non-Gaussian characteristics present in the data, the final formula of the model is complex, its identification is challenging and requires specific, suitable to heavy-tailed processes, statistical methods. The paper provides two kind of novelties — first of all, it uses real data from condition monitoring systems and our findings may be novel and surprising to predictive maintenance community, secondly — processing such specific data opens new areas for general data modelling and highlight novel research directions.

Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearings Faults in Induction Motors.

The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed in which sensors are the key to providing relevant information to verify the system. Recently, the smart sensor concept is common, in which the sensors are integrated with a data processing unit executing dedicated algorithms used to generate meaningful information about the system in situ. Additionally, infrared thermography has gained relevance in monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing, the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way in industrial applications. Therefore, in this manuscript, the structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: an infrared primary sensor, a preprocessing module, an image processing module, classification of faults, and a user interface. The infrared primary sensor considers a low-cost micro thermal camera for acquiring the thermal images. The processing modules and the classification module implement the data processing algorithms into digital development boards, enabling smart system characteristics. Finally, the interface module allows the final users to require the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be provided by the system. The smart sensor is validated in a real experimental test bench, demonstrating its capabilities in different case studies.

THE USE OF SPECIAL KNOWLEDGE IN THE INVESTIGATION OF EXPLOSIONS OF GAS AND DUST MIXTURES IN COAL MINES

The article is devoted to the generalization of approaches to conducting forensic mining examinations in the investigation of explosions of gas-dust mixtures in coal mines. The practical significance of the obtained results is the use of special practical knowledge of mining and other types of forensic examinations (trasological, petrochemical, firetechnical, etc.) in the investigation of explosions of gas-dust mixtures in coal mines, which will ensure rapid, complete, and safe investigation. A characteristic feature of explosion studies is that conclusions about reliable circumstances, causes and consequences of emergency situations can often be made with the combined use of classical mining, forensic and other expert techniques. General conclusions of mining engineering and other types of expertise (trasological, petrochemical, fire-technical, etc.) in the study of explosions make it possible to: determine the technical condition of workings, machines, mechanisms and equipment; establish reliable circumstances, causes and consequences of emergency situations; consider causal relationships between the actions of specific individuals and the requirements of regulatory legal acts on labor protection. Key words: coal mine, accident, gas-dust mixture explosion, high-temperature flame front, toxic and harmful combustion products, emergency, investigation, air wave.

Magnetically Levitated Flexible Vibration Sensors with Surficial Micropyramid Arrays for Magnetism Enhancement.

Magnetically levitated vibration sensors possess wide frequency response ranges and high sensitivity. Compared with springs and cantilevers, the levitated magnet suffers no mechanical abrasion, allowing minimized mechanical fatigue after prolonged exposure to vibration. However, magnetic levitated sensors are mostly based on fully rigid components, which are difficult to match the soft and curvilinear surface of the biological tissues and machines. Here, an innovative vibration sensor based on magnetic levitation has been developed. The proposed sensor contains two parallel magnetic membranes, one of which is levitated by magnetic force and connected to a specially designed sensor package. The surfaces of the membranes are modified with micropyramid arrays to enhance the magnetism and integrated with flexible coil arrays to maximize the changes in magnetic flux during vibration. The sensor exhibits a wide frequency response ranging from 1 Hz to 20 kHz and high sensitivity of 0.82 mV/μm at an operating frequency of 120 Hz. Various applications have been demonstrated through bone-conducted speech acquisition, sound recording, human motion detection, and machine condition evaluation. The sensor is one of the first flexible vibration sensors based on magnetic levitation. Its innovative levitated sensing structures may inspire development of novel flexible sensors with soft mechanical moving structures for force and displacement sensing in healthcare and industrial monitoring.

Machine Health Management System Using Moving Average Feature With Bidirectional Long-Short Term Memory

Abstract In today's highly competitive industrial environment, machine health management systems become a crucial factor for sustainability and success. The traditional feature extraction methods to reveal the health condition of the machine are labor-extensive. They usually depend on engineered design features, which require an expert knowledge level. Inspired by the successful results of deep-learning approaches that redefine representation learning from raw data, we propose moving-averaged features-based on Long-Short Term Memory (MaF-LSTM) networks. It is a hybrid approach that combines engineered features design with self-feature learning for the purpose of machine condition monitoring. First, features from overlapped sliding windows of the input time-series signals are extracted. Then, a moving-average filter is applied on the top of the generated features to enhance the feature’s condition indicter’s content. Next, a bidirectional LSTM is applied to learn the feature representation from the moving-averaged features. Two experiments, namely, bearing fault diagnosis and hydraulic accumulator fault detection, are implemented to verify the effectiveness of the proposed MaF-LSTM. The experimental results demonstrated that the proposed method outperforms all traditional condition monitoring methods in both use cases.

PIV Measurement and Proper Orthogonal Decomposition Analysis of Annular Gap Flow of a Hydraulic Machine

The fluid stress or flow-induced vibration of annular gap flow always has some influence on the stable working conditions of a hydraulic machine. A time-averaged analysis of flow may not have to explicitly acknowledge these factors. Accordingly, a finite-axial-length annular gap was measured via particle image velocimetry (PIV), with inner boundary motion and a stable outer boundary. As a statistic result regarding the fluid stress, the Reynolds stresses soared in the first region, were sustained in the middle region, but decreased at last. The flow had a higher convective transportation intensity in the radial direction than in other directions. Flow diagnostics were also performed by proper orthogonal decomposition (POD). As a result, the coherent structures were found. Then, the power spectrum density (PSD) functions were also calculated for finding the flow-induced vibration characteristics; the functions had high amplitude in the low-frequency domain and low amplitude in the high-frequency domain, with an order of magnitude between the two amplitudes of 10−1 to 10−2. In addition, the frequency was higher at a smaller gap width in the middle-frequency domain, but the condition was the opposite in the high-frequency domain. In conclusion, the fluid stresses were changeable and uneven along the flow direction, and flow-induced vibration obviously existed. Remarkably, the turbulence characteristics of the annular gap flow were not “laminar approximating,” while the diameter ratio of the gap was 0.6 to 0.8.

Modeling and numerical methods for studying the movement of a semi-mounted cotton harvesting machine

The paper deals with the issues of modeling the movement and rolling conditions of semi-mounted cotton harvesting machine of the MX and XM series (MX-1,8; MX-2,4; XM-01; XMG-04; XM-2,8). To solve this problem, the equations of motion of a cotton harvesting machine are compiled, using the example of the MX-2,4 brand, using Lagrange equations of the second kind. The Runge-Kutta numerical method solves the equations of horizontal and vertical vibrations of the machine with an uneven mass distribution between the front driving and rear driven wheels, and also calculates the wheel adhesion coefficient.

Health Condition Monitoring and Control of Vibrations of a Rotating System through Vibration Analysis

This paper presents condition monitoring, fault detection, and control of vibration of a rotating system using vibration analysis. The system includes ball bearings, pulleys, impellers, shafts, and an electric motor. The experimental approach generated different levels and forms of vibration that were measured using a vibration analyzer (CSI 2140). A vibration analyzer is used for determining the characteristics and nature of vibration of the experimental system. System vibrations were measured in terms of amplitude, phase angle, and frequency during the experimental operating conditions. After that, the experimental results were compared with the ideal working conditions mentioned in the ISO standard for the determination of machine health and condition monitoring. This paper also presents a passive control technique to enhance the machine’s health condition. The generated faults of the system have been detected and solved by using an exact measurement of polar plot analysis. This approach of condition monitoring, fault detection, and improving operating conditions can be used to visualize and analyze vibration data in any rotating system. Furthermore, this technique would be a feasible and effective way of finding strategies for the maintenance of rotary machines.

Accurate vibration-free robotic milling electric discharge machining

Traditional computer numeric control (CNC) machines have high accuracy but are limited by workpiece size and axes. Therefore, many researchers attempted to equip six-axis industrial robots (IRs) with milling end-effectors to explore the robot’s large and flexible working envelope. However, IRs lack stiffness and have limited force, resulting in low accuracy, poor surface roughness (SR), and low material removal rate (MRR). On the contrary, electric discharge machining (EDM) is a non-conventional process capable of shaping complex profiles in any conductive material. Since the EDM process has no physical contact between the electrode and the workpiece, it can machine diverse hard-to-cut materials accurately and free of vibration. However, to this day, EDM is found in limited conditions of CNC machines only. Thus, this research investigates a synergistic combination of IR and milling EDM. Adopting advanced CAM tools and offline programming, it examines pre-designed electrodes working analogue to conventional milling to perform the desired machining by intricate cutting paths. The results deliver a robotic machining technique able to cut hard materials using small industrial robots yet free of vibration and not pose dependent.

An Intelligent Anomaly Detection Method for Rotating Machinery Based on Vibration Vectors

Anomaly detection is a common technology for condition monitoring that is important for safe and reliable production in industry. In this work, we proposed an anomaly detection method for rotating machinery based on vibration vectors,whichwas inspiredby Polar plot in vibration analysis. The vibration vector, consisting of amplitude and phase, was selected as monitored indicator at different characteristic frequencies (CFs). The original vibration vectors obtained by fast Fourier transform-based order analysis (FFT-OA) were mapped from Polar to Cartesian plot for simplified calculation and better visualization. The acceptance region defined to distinguish anomalies from normal conditions was learned by support vector data description (SVDD) model with normal vibration samples. We proposed the anomaly score to indicate the degree of deviation from normal condition, and the fit degree to reduce underfitting of data description model. Two datasets, obtained from a real steam turbine and a real steam feed pump respectively, were used to validate the method under constant and variable speed operation conditions. Compared to the amplitude-based methods, the proposed method exhibited an extremely strong ability to detect anomalies in advance. Moreover, SVDD performed better than the minimum covariance determinant (MCD) and isolated forest (iForest) methods in terms of describing the acceptance region in the present work. With low timeconsuming calculation, the acceptance region can be self-evolving easily and has a prospect to the edge computing for machine condition monitoring.

A novel similarity-based status characterization methodology for gear surface wear propagation monitoring

The gearbox is a vital component for rotating machinery and has been used in many critical engineering applications. Surface wear is a common but inevitable phenomenon during the lifespan of the gearbox. Its propagation can result in some catastrophic failures and cause unexpected economic loss. Therefore, it is vital to evaluate the degradation process of the gear system caused by surface wear propagation in order to make reliable predictive maintenance-based decisions to ensure the safe operation of the gearbox transmission system. The vibration analysis technique is a prevailing tool for rotating machine condition monitoring. However, research on vibration-based gear wear monitoring is relatively rare as the dynamic interactions between gear surface wear and gear system dynamic characteristics would produce complex gear dynamic responses and vibration features. Therefore, this paper presents a novel similarity-based status characterization methodology for gear wear monitoring. In this proposed methodology, a novel gear wear monitoring indicator is developed to evaluate gear tooth contact characteristics at different wear severities, which could significantly benefit gear systems' health management. The effectiveness of the proposed method for gear wear propagation process monitoring is presented and proven through a series of run-to-failure tests with different lubrications and operational conditions.

Novel Way to Apply Transfer Learning to Aircraft System Fault Diagnosis

In recent years, transfer learning as a method that solves many issues limiting the real-world application of conventional machine learning methods has received dramatically increasing attention in the field of machine fault diagnosis. One major finding from an initial literature review shows that the majority of the existing research only focus on the transfer of diagnostic knowledge between various conditions of the same machine or different representation of similar machines. The primary goal of the current work is to seek a way to apply transfer learning to distinct domains, thereby expanding the boundary of transfer learning in the fault diagnosis field. In particular, attempts will be made to explore ways of transferring knowledge between diagnostic tasks of different aircraft systems. One promising method to help achieving this goal is transfer learning by structural analogy, since this method is capable of extracting high-level structural knowledge to apply transfer learning between seemingly unrelated domains, similar to the scenarios of transfer between different aircraft systems.