Failure Of Machinery Research Articles

DNA Damage: From Threat to Treatment.

DNA is the source of genetic information, and preserving its integrity is essential in order to sustain life. The genome is continuously threatened by different types of DNA lesions, such as abasic sites, mismatches, interstrand crosslinks, or single-stranded and double-stranded breaks. As a consequence, cells have evolved specialized DNA damage response (DDR) mechanisms to sustain genome integrity. By orchestrating multilayer signaling cascades specific for the type of lesion that occurred, the DDR ensures that genetic information is preserved overtime. In the last decades, DNA repair mechanisms have been thoroughly investigated to untangle these complex networks of pathways and processes. As a result, key factors have been identified that control and coordinate DDR circuits in time and space. In the first part of this review, we describe the critical processes encompassing DNA damage sensing and resolution. In the second part, we illustrate the consequences of partial or complete failure of the DNA repair machinery. Lastly, we will report examples in which this knowledge has been instrumental to develop novel therapies based on genome editing technologies, such as CRISPR-Cas.

Anomaly monitoring improves remaining useful life estimation of industrial machinery

Estimating remaining useful life (RUL) of industrial machinery based on their degradation data is very critical for various industries. Machine learning models are powerful and very popular tools for predicting time to failure of such industrial machinery. However, RUL is ill-defined during healthy operation. This paper proposes to use anomaly monitoring during both RUL estimator training and deployment to tackle with this problem. In this approach, raw sensor data is monitored and when a statistically significant change is detected, it is taken as the degradation onset point and a data-driven RUL estimation model is triggered. Initial results with a simple anomaly detector, suited for non-varying operating conditions, and multiple RUL estimation models showed that the anomaly triggered RUL estimation scheme enhances the estimation accuracy, on in-house simulation and benchmark C-MAPSS turbofan engine degradation data. The scheme can be employed to varying operating conditions with a suitable anomaly detector.

Sistemas de gestión en mantenimiento: propuesta de un TPM en una empresa productora de marcos y molduras de la región

This research is carried out in a company that produces frames and moldings and addresses the need to develop a Total Productive Maintenance (TPM) program, as a result of the unavailability of machinery and equipment; and training that affects maintenance operations, which are not performed correctly causing costs. Currently there is a 76% availability of machinery; 78% in equipment; 42% in training and monthly average costs in machinery of $ 15,260 pesos; and in equipment of $ 1,860 pesos, in terms of maintenance costs there are no globales records. The objective was to carry out a proposal for maintenance activities, through the TPM methodology; to have an updated maintenance program. The procedure was: Describe the area under study; describe the situation of the area under study; establish TPM policies and goals; identify failures in machinery and equipment; and develop the maintenance program. It was contributed with the contribution of a maintenance program composed of: calendar, records of equipment in stock, corrective and preventive maintenance, maintenance scheduling, records of maintenance costs and catalog. Thus fulfilling the objective of this investigation.

Use of contactless methods and means for checking the quality of agricultural machinery spare parts

Quality control of spare parts of agricultural machinery has now acquired significant relevance, which is due to the presence of a large share of low-quality products on the market. The use of low-quality spare parts increases the risk of failure of the equipment in use, which lead to its downtime and financial losses of the agricultural producer, which is highly undesirable in the modern realities of the domestic agricultural industry. Contactless methods and tools for quality control of spare parts will improve the accuracy and productivity of control. (Research purpose) The research purpose is in developing a device that allows to control the geometric, physical and mechanical parameters of spare parts, the use of which should increase the performance and accuracy of control due to a comprehensive non-contact method of measuring spare parts. (Materials and methods) Authors applied a non-contact triangulation method for controlling geometric parameters and a X-ray fluorescence method for controlling physical and mechanical parameters. (Results and discussion) The article presents the devices that are closest in technical parameters and based on them an automated measuring device using methods and means that allows controlling the geometric, physical and mechanical parameters of the object. The article presents the principle of operation and functions of an automated measuring device. The article describes the factors that affect the control process by an automated measuring device. (Conclusions) It was found that the use of contactless methods and quality control tools for spare parts of agricultural machinery could improve the accuracy and productivity of control and eliminate the influence of human factors on the measurement process. Timely quality control of spare parts of agricultural machinery using an automated measuring device will allow to identify low-quality or counterfeit spare parts before installation on the repaired equipment, which will reduce the risk of failures of agricultural machinery.

The Condition Based Monitoring for Bearing Health

Bearing is a small component that widely uses in industries, either in rotary machines or shafts. Faulty in bearing might cause massive downtime in the industries, which lead to loss of revenue. This paper intends to find the consequential statistical time-domain-based features that can be used in classification from accelerometry signals for the bearing condition. An accelerometer was used as the data logger device to attain the condition signals from the bearing. Machinery Failure Prevention Technology (MFPT) online dataset has three different bearing conditions: baseline condition, inner faulty condition, and outer faulty condition. Extraction of eight statistical time-domain features was done, which is root-mean-square (RMS), minimum (Min), maximum (Max), mean, median, standard deviation, variance, and skewness. The identification of informative attributes was made using a filter-based method, in which the scoring is done by using the Information gain ratio. For the extracted features, the data splitting of training data to testing data was set to the ratio of 70% and 30%, respectively. The selected feature for classification is then fed into various types of classifiers to observe the effect of this feature selection method on the classification performance. From this research, six features were identified as the significant features: variance, standard deviation, Min, Max, mean, and RMS. It is said that the classification accuracy of the training data and the testing data using the filter-based feature selection method is equivalent to the classification accuracy of all the features selected.

Fuzzy expert analysis of the severity of mining machinery failure

Mining machinery failure is almost an everyday occurrence. Usually the failures bare certain consequences, which require additional financial costs to repair and restore the system to its operational state. The consequences are viewed through negative and damaging effects a failure has on the machine, health and safety of the employees, work environment, and on the environment. The removal of the consequences of the failure requires additional financial investment, which has a negative impact on the company’s business. In order to prevent this, it is necessary to have Risk Centered Maintenance, where the risk assessment would include all the negative consequences of the risky event. The fuzzy expert assessment is presented in this paper, as well as the failure severity assessment based on the harmful effects of the failure. The negative effects of the machine component failure, such as the time needed for the repair, the possibility of workplace injury caused by the failure, and the impact it has on the environment, are analyzed in this paper as well. This approach to the severity of failure assessment enables a more comprehensive view of this risk indicator. It also enables the risk indicator “severity of failure” to gain greater significance in combination with other risk indicators. The developed model was presented on the example of typical failures of the hydraulic subsystems of a mobile crushing machine Lokotrack LT 1213S.

Superior Slurry Erosion Behavior of a Casting NiCoCrFeNb0.45 Eutectic High Entropy Alloy

The flow passage components in hydraulic machinery will be eroded rapidly when running in sand water. The wear resistance of materials commonly used in hydraulic machinery needs to be improved to prevent the failure of hydraulic machinery. New wear-resistant materials are urgently needed to improve the service life of hydraulic machinery. Eutectic high entropy alloy (EHEA) is a new kind of alloys with in situ lamellar structure. NiCoCrFeNb0.45 EHEA has combined excellent strength and toughness. In this study, slurry erosion tests of NiCoCrFeNb0.45 EHEA were carried out with different impact angles and impact velocities using a rotary jet erosion test apparatus. The NiCoCrFeNb0.45 EHEA exhibited significant slurry erosion resistance compared with medium carbon steel 1045 and stainless steel 04Cr13Ni5Mo. The erosion rate of EHEA is much slower. Compared with medium carbon steel and stainless steel, the erosion of EHEA is completed via the accumulation and removal of platelets after plowing and work hardening, accompanied by partial micro-cutting.

Rotary machine vibration monitoring and smart balance correction

During the rotary machine operation process, seemingly small amounts of abnormal vibration can often cause serious damage to the machinery over time and even increase the risk of accidents. Although professional vibration engineers can determine the current health status of a machine by interpreting the vibration spectrum information and predicting which components will fail, if even ordinary operators can send feedback regarding the vibration signals reaching the human–machine interface through a system when an abnormality is detected in the machine, the abnormality can be made known and processed in time. This can prevent the magnified impact of rotary inertia, thereby lowering the risk of major damage and the failure of machinery and equipment, as well as effectively saving on equipment maintenance costs. This study mainly adopted LabVIEW and Arduino IDE to develop a control program and human–machine monitoring interface. As the initial experiment on rotary machine vibration monitoring and smart balance correction, the measurement system setup in this study was applied to determine vibration abnormality as well as to carry out continuous online automatic balance correction. Experimental verification was carried out using active correction and smart correction. In terms of active online balance correction, the amplitude correction rate was 96%, the double-frequency correction rate was 102.9%, and the correction process was performed in 5 min. In terms of smart balance correction, the amplitude correction rate was 103.8%, the double-frequency correction rate was 103.3%, and the correction process was performed in 3 min. Through feedback signaling, the operator can effectively learn the current health status of the mechanical equipment from the human–machine interface.

Theoretical and experimental studies to predict vibration responses of defects in spherical roller bearings using dimension theory

Abstract A frictionless force transmission between the mechanical components of high production volume systems was accomplished by Rolling Element Bearings, which leads to being an imperative item of the condition-based maintenance (CBM). A prominent fault would cause malfunction, and its substantial growth may lead to catastrophic failure of such machinery ultimately give rise to unscheduled maintenance, and in extreme cases turns into a quite expensive shut down of the same. These decisive situations explore a growing demand for a robust failure diagnosis scheme for bearings. The present work demonstrates a novel approach to develop a dynamic model for vibration response of spherical roller bearings using dimensional analysis with Buckingham’s pi theorem (BPT) by considering significant geometric, operating and thermal parameters of the system. The results obtained have been validated with experiments performed on the developed test rig under diverse operating conditions. Vibration amplitudes considerably enhanced in the presence of rotating speed, applied load, bearing temperature, and volume of defect. Multivariable regression analysis is performed to reveal the effectiveness of the model for precise detection of impending bearing failure. The vibration response obtained by the DA model, experimental runs and MVRA are agreeing with physical perceptive and potentials. Results indicate the simplicity and reliability of the approach.

Fuzzy Model for Risk Assessment of Machinery Failures

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

Declining Social Control and the Rising Deviant Behaviour in India

Crime rates are increasing across the Indian society. Normally, such crimes are attributed to two broad categories of factors: (a) psychological factors like individual or mob fury and (b) administrative factors like the failure of law and order machinery. These explanations, however, do not account for the increasing rates of such demeaning instances. This article, attempting to explain the increasing crime rates, focuses on the social control theory. The main argument of the article is that the rapidly declining informal social control causes the phenomenal rise of decadent behaviour in the contemporary Indian society. Due to modernising forces, traditional social institutions and structures such as family, kinship, caste system and village community have become weak. As a result, the traditional informal social control based on shame has gradually diminished. And the sense of guilt, the functional alternative to shame, as an informal mechanism of social control, has not yet been fully institutionalised. This seems to be the major factor giving rise to widespread deviant behaviour in India. Although formal mechanisms of social control like police and judiciary do exist, they cannot be very effective without being reinforced with the informal social control.

Ensemble Fog Computing Architecture for Unstable State Detection of Hydraulic System

Abstract Failure of machinery and equipment in factories, plants, etc. is a great loss to the operator. Various studies have been conducted for a long time to solve this problem, and many solutions have recently been developed to perform real-time health monitoring by attaching sensors to facilities. In addition, machine learning has allowed more accurate diagnosis of the condition to be carried out on these collected data. However, real-time fault detection is accompanied by a number of difficulties. Class imbalance issues are considered one of the most representative factors that makes it difficult to apply machine learning models in real-world locations. Class imbalance means that when it comes to classification problems, the amount of data in one class is greater than that in another class. In this case, the machine learning model is overfitted to a class with a lot of data, causing performance degradation. In real-world sites, the performance degradation of the model due to class imbalance is very serious because most data occurs in normal state, while fault data does not occur very well. Several prior studies have proposed ways to overcome this class imbalance, but each has several limitations. In this paper, an algorithm solution method and computing architecture solution method are introduced together as methodologies to overcome class imbalance, and a robust classification model is designed for class imbalance.

Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training

To promote the progress of fault diagnosis system, this study proposes an intelligent and effective fault diagnosis algorithm based on data-driven. Firstly, we propose a new time-frequency analysis method named second-order time-reassigned multisynchrosqueezing transform (STMSST) based on Gaussian-modulated linear group delay (GLGD) model to deal with the vibration signals of fault object for obtaining time-frequency images with high resolution. Then, an improved training method named evenly mini-batch training method is combined with convolutional neural network (CNN) to train and learn fault features from those obtained time-frequency images. Further, the proposed fault diagnosis algorithm is tested on the Case Western Reserve University (CWRU) bearing dataset and the Machinery Failure Prevention Technology (MFPT) Society dataset, respectively, and the experimental results indicate that the feature representation and training effect in our method is superior to state-of-the-art fault diagnosis methods. Finally, the proposed method is applied on the loudspeaker pure-tone detection dataset, which achieves the loudspeaker anomaly diagnosis, and the diagnosis result has verified that the method can meet the needs of practical engineering.

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components.

Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

Application of optimised neural networks models in gears and bearings faults diagnosis

Gears and bearings are some of the most important machine components in the industrial world and detection of their faults has become a major trend. In the present article, in order to bring a reliable methodology for monitoring and diagnosis of rotating machinery failures, a test rig is implemented. However, to diagnose gears and bearings combined faults, a monitoring system based on neural network model (NNM), is proposed. To train and test the NNM, the principal high frequency indicators, determined with the collected time domain vibration data, and codes of defects are used respectively as input and output data. A comparison of two learning algorithms, optimised by the Taguchi method, was done to determine the best NNM. Therefore, the proposed method is effective to study other various industrial cases.

Application of optimised neural networks models in gears and bearings faults diagnosis

Gears and bearings are some of the most important machine components in the industrial world and detection of their faults has become a major trend. In the present article, in order to bring a reliable methodology for monitoring and diagnosis of rotating machinery failures, a test rig is implemented. However, to diagnose gears and bearings combined faults, a monitoring system based on neural network model (NNM), is proposed. To train and test the NNM, the principal high frequency indicators, determined with the collected time domain vibration data, and codes of defects are used respectively as input and output data. A comparison of two learning algorithms, optimised by the Taguchi method, was done to determine the best NNM. Therefore, the proposed method is effective to study other various industrial cases.

Selection for tandem stop codons in ciliate species with reassigned stop codons.

The failure of mRNA translation machinery to recognize a stop codon as a termination signal and subsequent translation of the 3’ untranslated region (UTR) is referred to as stop codon readthrough, the frequency of which is related to the length, composition, and structure of mRNA sequences downstream of end-of-gene stop codons. Secondary in-frame stop codons within a few positions downstream of the primary stop codons, so-called tandem stop codons (TSCs), serve as backup termination signals, which limit the effects of readthrough: polypeptide product degradation, mislocalization, and aggregation. In this study, ciliate species with UAA and UAG stop codons reassigned to code for glutamine are found to possess statistical excesses of TSCs at the beginning of their 3’ UTRs. The overrepresentation of TSCs in these species is greater than that observed in standard code organisms. Though the overall numbers of TSCs are lower in most species with alternative stop codons because they use fewer than three unique stop codons, the relatively great overrepresentation of TSCs in alternative-code ciliate species suggests that there exist stronger selective pressures to maintain TSCs in these organisms compared to standard code organisms.

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.