Condition Monitoring System Research Articles

Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection

Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors’ data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.

Reliability, availability, and condition monitoring of inverters of grid‐connected solar photovoltaic systems

AbstractReliability, Availability and Condition Monitoring (RACM) evaluation has become a critical area of interest for researchers as the output power quality of a Photo‐Voltaic (PV) system depend on the reliability of its components. In this paper, the RACM of grid‐connected PV systems is presented. For this, the Reliability Block Diagram (RBD) technique along with the exponential probability distribution function is used. The main objective of this work is to identify the weakest subsystem of a system in order to enhance system reliability. The condition monitoring system will be helpful in avoiding the sudden breakdown and unexpected maintenance of the system. An elaborate analysis is presented for PV system beginning from the sub‐assemblies to the subsystems and then to the overall system. In addition, the subsystems are ranked based on their impact on the overall availability of the system. This is decided based on the availability importance measures. It is observed that the inverter forms the weakest subsystem in the solar system. The novelty of the proposed RACM method is that one can monitor the health status or useful life of the system regularly which is not yet explored in the literature. Most of the papers have considered that the components of PV systems are non‐repairable but in this paper, the redundancy concept is explored which helps in increasing the reliability and availability of the system. When the proposed method and the results obtained are compared to existing methods, it is discovered that the proposed work outperforms them.

A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app.

Methodology of Analysis of Thermal Vision Parameters of NPP Standby Diesel Together with Standard Instrument Readings

Analytical studies of the results of thermal imaging control using IAS TIK together with the corresponding values of the temperature control results obtained in the standard DGS condition monitoring system were carried out within the framework of R&D for the development of an integrated automated system for thermal imaging control (IAS TIK). Approbation of IAS TIK in NPP conditions was carried out by thermography of standby diesel generator sets of NPP type 12zv40/48+S2445-12. At the same time, the parameters of the diesel engine and the generator were measured using the standard control system. The methodology is proposed that provides an analysis of the uniformity of the operation of diesel cylinders, the condition of the crankshaft bearings, the condition and quality of the generated electricity of the generator, as well as the analysis of the operation of lubrication and cooling systems. The practical application of the technique demonstrates the correspondence between the temperatures measured by the standard system and the IAS TIK. A joint analysis of the results obtained by the standard and developed systems provides an increase in the depth of the diagnostic examination and confirms the information content of the thermographic parameters and the quality of the measurement channels of the standard systems. The parameter values obtained from the analysis of the indicators of the standard control system, recorded at a steady power value, will be used in subsequent diagnostics to establish trends in these parameters

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

An Unsupervised Vibration Noise Reduction Approach and Its Application in Lubrication Condition Monitoring

Accelerometers are sensitive devices that capture vibrational fault signatures from industrial machines. However, noise often contaminates these fault signatures and must be eliminated before analysis. A data-driven (DD) denoising algorithm capable of filtering useful vibrational fault signatures from background noises was derived in this study. The algorithm was first validated by comparing its denoised result with a numerically generated ideal signal with a known exact solution. The DD denoising approach reduced the Mean Squared Error (MSE) from 0.459, when no denoising was performed, to 0.068, indicating an 85.2% decrease in noise. This novel approach outperformed the Discrete Wavelet (DW) denoising approach, which had an MSE of 0.115. The proposed DD denoising algorithm was also applied to preprocess vibration data used for the real-time lubrication condition monitoring of the plastic injection molding machine’s toggle clamping system, thereby reducing false positive relubrication alarms. The false positive rates, when analysis was performed on the raw vibration and the DW denoised vibration, were 10.7% and 7.6%, respectively, whereas the DD denoised vibration yielded the lowest false positive rate at 1%. This low false positive rate of the DD denoised vibration indicates that it is a more reliable condition monitoring system, thereby making this technique suitable for the smart manufacturing industry.

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies.

Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.

Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification

The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach.

Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

Edge-Intelligence-Based Condition Monitoring of Beam Pumping Units Under Heavy Noise in Industrial Internet of Things for Industry 4.0

Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.

Adaptive Sampling for Monitoring Multi-Profile Data with Within-and-between Profile Correlation

Multi-profile data can provide within-and-between profile information for efficiently modeling and monitoring system status. In practice, however, acquisition of such data requires large number of sensors, which raises various concerns and difficulties, for example, cost, energy, and data transmission bandwidth, in accessing the full data from each sensor. In this article, we propose an adaptive sampling strategy for multi-profile monitoring by using limited portion of data. The proposed sampling and monitoring scheme incorporates the within-and-between profile correlation and features the balance between random search and greedy search in identifying the most informative profiles. More specifically, the multivariate functional principal component analysis (MFPCA) is used to capture the within-and-between profile correlation, and the MFPC scores are augmented for unobservable profiles to feed into a multivariate CUSUM chart. Two properties of the proposed method for allocating sampling resources among sensors are investigated. Numerical and case studies are conducted under various scenarios to demonstrate the effectiveness of the method.

Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Abstract Removing noise from health signals is critical in gas path diagnostics of aircraft engines. An efficient noise filtering/denoising method should remove noise without using future data points, preserve important changes, and promote accurate diagnostics without time delay. Machine learning (ML)-based methods are promising for high fidelity, accuracy, and computational efficiency under the motivation of Intelligent Engines. However, previous ML-based denoising methods are rarely applied in actual engineering practice because they cannot accommodate time series and cannot effectively capture important changes or are limited by the time delay problem. This paper proposes a convolutional neural network denoising auto-encoder (CNN-DAE) method to build a denoising auto-encoder structure. In this structure, a convolutional operation is used to accommodate time series, and causal convolution is introduced to solve the problem of using future data points. The proposed denoising method is evaluated against NASA's propulsion diagnostic method evaluation strategy (prodimes) software. It has been proved that the proposed method can accommodate time series, remove noise for improved denoising accuracy, and preserve the important changes for enhanced diagnostic information. NASA's blind test case results show that kappa coefficient of a common diagnostic method using the processed data is 0.731 and is at least 0.046 higher than the other diagnostic methods in the open literature. Processing health signals using the proposed method would significantly promote accurate diagnostics without time delay. The proposed method could support intelligent condition monitoring systems by exploiting historical information for improved denoising and diagnostic performance.

Sensor integrating plain bearings: design of an energy-autonomous, temperature-based condition monitoring system

Plain bearings are increasingly being used under mixed friction, resulting in a higher risk of wear-induced failure. Therefore, the relevance of condition monitoring is increasing, which can enable the early detection of wear inducing conditions. The presented work focuses on the development of an energy-autonomous, temperature-based condition monitoring system for plain bearings. The components of the condition monitoring system are to be integrated directly into the machine element and operating data to be transmitted wirelessly.The fundamental monitoring approach pursued, is the in-operando calculation of the lubrication gap height via the standardized Gümbel-curve based on the temperature map in the plain bearing. If the gap height undercuts a critical threshold, the plain bearing will operate in mixed friction. Thus, representing a potentially wear-causing operating point. The energy required for operating the system is harvested thermoelectrically within the plain bearing via thermo-generators, which use the temperature gradients in the plain bearing during operation.In this work, the simulative evaluation of the harvestable energy will be presented. The usable temperature difference will be determined for varying positioning of the energy harvester. The results obtained allow the subsequent manufacture of a first Sensor integrating Machine Element (SiME)-prototype. In this context a feasible setup regarding the types and number of sensors as well as a microcontroller for processing the measured data will be defined with respect to the harvestable energy. Furthermore, the principle for temperature-based gap height determination will be introduced, applied for different operating points and compared to simulative results.

Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark

A wind turbine (WT) is an electromechanical system that often operates under a wide range of production conditions. These electrical systems are nowadays expanding rapidly, and they have considerable importance due to their efficiency as renewable energy sources. This led to proposing an innovative and efficient solution with intelligent systems to maintain and ensure the safe and stable operation of these dynamic systems. Maintenance tasks are based on the development of high-performance diagnostic tools, which consist in detecting and locating correctly and upstream the various failures affecting this wind machine. Where, the condition monitoring and supervision systems must rely on reliable fault diagnosis techniques in order to: avoid breakdowns and unscheduled shutdowns, improve their operation, and increase their energetic performances. In order to ensure adequate maintenance actions for the wind system, the purpose of this article is to propose and develop a robust and intelligent fault diagnosis structure. In this work, Kalman filters (KF) as state estimators are used to observe the output states of the sub-systems in order to generate the appropriate residuals evaluated by predetermined thresholds. Adaptive and hybrid network-based fuzzy inference systems (ANFIS) have been employed for the evaluation and classification stages of the detected faults to minimize the degradation of the wind turbine. All possible faults of wind turbine systems, sensors, and actuators are tested and investigated in all parts: pitch angle systems, drive, and generator with converter. The developed fault detection and identification structure are tested on a horizontal WT benchmark model using different scenarios and faults. The simulation results show the ability of the proposed and developed diagnostic methodology to detect the faults occurring efficiently and correctly in the machine. Thus, by using this robust diagnostic strategy, the condition monitoring system can maintain and ensure stable and safe operation to generate sufficient electrical power.

Wearable and battery-free wound dressing system for wireless and early sepsis diagnosis.

Sepsis is a severe organ dysfunction typically caused by wound infection which leads to septic shock, organ failure or even death if no early diagnosis and property medical treatment were taken. Herein, we report a soft, wearable and battery-free wound dressing system (WDS) for wireless and real-time monitoring of wound condition and sepsis-related biomarker (procalcitonin [PCT]) in wound exudate for early sepsis detection. The battery-free WDS powered by near-field communication enables wireless data transmission, signal processing and power supply, which allows portable intelligent wound caring. The exudate collection associates with soft silicone based microfluidic technologies (exudate collection time within 15 s), that can filtrate contamination at the cell level and enable a superior filtration rate up to 95% with adopting microsphere structures. The battery-free WDS also includes state-of-the-art biosensors, which can accurate detect the pH value, wound temperature, and PCT level and thus for sepsis diagnosis. In vivo studies of SD rats prove the capability of the WDS for continuously monitoring wound condition and PCT concentration in the exudate. As a result, the reported fully integrated WDS provides a potential solution for further developing wearable, multifunctional and on-site disease diagnosis.

Design of Equipment Fault Diagnosis System Based on Audio Analysis Technology

In the aspect of state monitoring of hydropower unit, the main technical means is to collect and monitor the data on site by deploying various sensors and cameras in the unit and its affiliated units, relying on computer monitoring system and unit state monitoring system. However, it is difficult to intuitively feel the audio characteristics of the unit when it is running. Therefore, an equipment fault diagnosis system based on audio analysis technology is proposed, which makes use of audio analysis technology to analyze and process the on-site sound online, and provides a new auxiliary monitoring means for the equipment running status. Furthermore, the effectiveness of the system is verified by the operation of a power plant.

On-Line Condition Monitoring System for DC-Link Capacitor of Back-to-Back Converters Using Large-Signal Transients

The increasing use of power electronic equipment and the importance of their continuous and correct operation have attracted remarkable attention to the condition monitoring of power electronic converters. Since capacitors are the leading cause of power electronic converters failure, this article attempts to attain a solution to monitor the condition of the dc-link capacitor in a three-phase back-to-back converter using the stimulated measurable transients. The proposed method works based on the temporary change of switching logic and the measurement of the dc-link voltage and converter input phase current transients. By processing the measured signals, the capacitance of the capacitor is calculated as an indicator of its degradation. The results of simulations and experimental studies are presented to verify the proficiency of the proposed method. Based on the results, the calculated capacitance is consistent with the value measured by an LCR meter with an estimation error of less than 1%.

Correlation transmissibility damage indicator for deterioration performance analysis of hydropower generator unit

The condition monitoring system of the hydropower generator unit (HGU) contains a vast amount of monitoring data related to equipment performance. It is challenging to reveal the HGU performance change law from these data. This paper constructed a correlation transmissibility damage indicator (CTDI) to analyze HGU degradation performance based on the transmissibility function (TF). Unlike traditional TF damage indicators, CTDI can use different types of monitoring data to obtain system TF to increase TF combination and improve detection sensitivity. The analysis is performed using actual monitoring data from a hydropower plant in Guangxi, China. Firstly, a new frequency concept is defined based on the characteristics of HGU obtaining data every 10 minutes. Then, the HGU is considered a system and divided into three subsystems according to the sensor installation locations of the monitoring system. The damage indicator is used to analyze the system and its subsystems at the time scales of day, week, and month to locate and quantify the damage of HGU. The final results of this analysis show that the method can accurately detect and locate faults, applies to different operating conditions, and reveals the HGU performance deterioration pattern, which is of great significance for HGU maintenance.