Automated methods for vibration-based condition monitoring of rotating machines

Abstract The sustainable and safe use of rotating machines can be enhanced by condition monitoring. Acceleration signals are commonly used for the indirect measurement of condition, but their analysis can be complicated in industrial applications. When the number of monitored targets is large, efficiently conducted data analysis is essential. The aim of this research was to develop automated, data-driven methods for the analysis of acceleration signals and related data acquired from rotating machines especially in real measurement environments. Methods that simplify system identification would ease the implementation of algorithms, while online monitoring benefits from methods that detect anomalies automatically. The proposed methods for system identification help to automate the selection of training samples, signal features and signal processing settings by optimizing computational criteria through data exploration. Two of the methods proposed for anomaly detection monitor the residuals of regression models and one applies an adaptive approach based on an autocorrelation-based criterion. Methods that need training data from a target in undamaged condition were studied by using real measurement data from azimuth thrusters and a roller leveler. The autocorrelation-based criterion developed for detecting local faults in slowly rotating rolling element bearings was studied with laboratory and simulation data. The results indicated that automated selection of training samples systematized the identification of anomaly detection models and their operating areas in the case of azimuth thrusters. Automated feature selection revealed previously unknown dependencies between acceleration signals and parameters, such as steel strip properties in roller leveling. In addition, certain patterns of local faults in slowly rotating rolling element bearings could be detected automatically from short time series that contained only a few fault impulses. The findings of this work can be useful in condition monitoring applications in real measurement environments, where repeatability and the automation and facilitation of data analysis are targeted.Tiivistelmä Pyörivien koneiden kunnonvalvonta voi parantaa niiden kestävää ja turvallista käyttöä. Kiihtyvyyssignaaleja käytetään tavallisesti kunnon epäsuorassa mittauksessa, mutta niiden analysointi voi olla monimutkaista teollisissa sovelluksissa. Kun valvottavia kohteita on useita, on olennaista suorittaa data-analyysi tehokkaasti. Tämän tutkimuksen tarkoituksena oli kehittää automaattisia dataan perustuvia menetelmiä pyörivistä koneista mitattujen kiihtyvyyssignaalien ja niihin liittyvän datan analysointiin erityisesti todellisissa mittausympäristöissä. Järjestelmän identifiointia yksinkertaistavat menetelmät voivat helpottaa algoritmien käyttöönottoa, kun taas jatkuvassa valvonnassa on hyötyä menetelmistä, jotka havaitsevat poikkeavuuksia automaattisesti. Järjestelmän identifiointiin ehdotetut menetelmät auttavat automatisoimaan opetusnäytteiden, signaalin piirteiden ja signaalin prosessointiasetusten valintaa optimoiden laskennallisia kriteerejä datan perusteella. Kaksi esiteltyä menetelmää poikkeavuuksien havaitsemiseen seuraa regressiomallien jäännösvirhettä ja yksi soveltaa mukautuvaa menetelmää, joka perustuu autokorrelaatiosta laskettuun kriteeriin. Menetelmiä, jotka tarvitsevat opetusdataa vauriottomasta kohteesta, tutkittiin todellisella mittausdatalla ruoripotkureista sekä rullaoikaisukoneesta. Hitaasti pyörivien vierintälaakerien paikallisten vikojen havaitsemiseen kehitettyä autokorrelaatioon perustuvaa kriteeriä tutkittiin laboratorio- ja simulointidatalla. Tulokset osoittivat, että opetusdatan automaattinen valinta systematisoi poikkeavuuksien havaitsemiseen kehitettyjen mallien ja niiden toiminta-alueiden identifiointia ruoripotkurien tapauksessa. Automatisoitu piirteiden valinta paljasti ennalta tuntemattomia riippuvuuksia kiihtyvyyssignaaleista ja parametreista, kuten rullaoikaistavan teräsnauhan ominaisuuksista. Lisäksi tietyt vierintälaakereiden paikallisten vikojen piirteet voitiin havaita automaattisesti lyhyistä aikasarjoista, jotka kattoivat vain muutaman vikaimpulssin. Työn tuloksia voidaan hyödyntää todellisten mittausympäristöjen kunnonvalvontasovelluksissa, joissa tavoitellaan toistettavuutta sekä data-analyysin automatisointia ja helpottamista

Roller leveler monitoring using acceleration measurements and models for steel strip properties

Abstract The advanced steel grades and high productivity requirements in the modern steel industry subject production machines to increased mechanical stresses, which inflicts losses. Novel data-oriented solutions to the monitoring of machines have a pivotal role in loss prevention, but the industrial data with high sampling rates, noise, and dimensions bring challenges there. This study proposes a new monitoring approach for roller levelers based on vibration measurements and regression models for estimating steel strip properties including yield strength, width, and thickness. The regression residuals are monitored based on moving mean and range charts, which reveal changes from the expected normal operation. A high-dimensional feature set of 144,000 statistical features was studied with various feature selection methods, including filters and wrappers. Multiple linear regression and generalized regression neural network were applied in modeling. The approach was validated using data from an industrial roller leveler processing steel strips with diverse properties. The results reveal that the accurate prediction of the strip thickness from the strip properties is possible and multiple linear regression was generally the superior model therein. Additional simulations indicated that the control charts can detect deviant operation. Supplemental information about the momentary operation of the machine would improve the approach

On training data selection in condition monitoring applications:case azimuth thrusters

Abstract Machine learning techniques are commonly used in the vibration-based condition monitoring of rotating machines. However, few research studies have focused on model training from a practical viewpoint, namely, how to select representative training samples and operating areas for monitoring applications. We focus on these aspects by studying training sets with varying sizes and distributions, including their effects on the models to be identified. The analysis is based on acceleration and shaft speed data available from an azimuth thruster of a catamaran crane vessel. The considered machine learning algorithm was previously introduced in another study suggesting it could detect defects on the thruster driveline components. In this work, practical guidance is provided to facilitate its implementation, and furthermore, an adaptive method for training subset selection is proposed. Results show that the proposed method enabled the identification of usable training subsets in general, while the success of the previous approach was case-dependent. In addition, the use of Kolmogorov–Smirnov or Anderson–Darling tests for normal distribution, as a part of the method, enabled selections that covered the operating area broadly, while other tests were unfavorable in this regard. Overall, the study demonstrates that reconfigurable and automated model implementations could be achievable with minor effort

Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

Abstract This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

Abstract The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties

Towards online adaptation of digital twins

Abstract Digital twins have gained a lot of attention in modern day industry, but practical challenges arise from the requirement of continuous and real-time data integration. The actual physical systems are also exposed to disturbances unknown to the real-time simulation. Therefore, adaptation is required to ensure reliable performance and to improve the usability of digital twins in monitoring and diagnostics. This study proposes a general approach to the real-time adaptation of digital twins based on a mechanism guided by evolutionary optimization. The mechanism evaluates the deviation between the measured state of the real system and the estimated state provided by the model under adaptation. The deviation is minimized by adapting the model input based on the differential evolution algorithm. To test the mechanism, the measured data were generated via simulations based on a physical model of the real system. The estimated data were generated by a surrogate model, namely a simplified version of the physical model. A case study is presented where the adaptation mechanism is applied on the digital twin of a marine thruster. Satisfactory accuracy was achieved in the optimization during continuous adaptation. However, further research is required on the algorithms and hardware to reach the real-time computation requirement

Probabilistic condition monitoring of azimuth thrusters based on acceleration measurements

Abstract Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application

Data analysis of a paste thickener

Abstract The solids content of slurry is typically increased in thickeners. A clean overflow and maximum solids concentration in the underflow are the general targets. The flocculant rate and underflow rate are the two independent variables that are typically used for control. The dependent variables include rake torque, underflow density, overflow turbidity, solids interface level (bed depth), solids inventory (bed pressure), solids settling rate and underflow viscosity. The research problem in question is that the outgoing paste is sometimes difficult to pump. The phenomena leading to this situation are not well known. In the worst-case scenario these phenomena cause clogging in the piping. A data analysis has been done to find the variables that affect and correlate with the pumping problem. The scope of this study covers the measurements from the feed line, thickener and underflow. The goal is to gain better understanding of the phenomena after this phase. The data analysis was done using the paste line pressure difference as a response variable and by dividing the data collected from Yara’s Siilinjärvi mill into two parts: operation areas with high and low pressure difference. The analysis is focused on Thickener 1 due to better availability of measurements. The knowledge of the variables found to influence the pressure difference can be utilized in further development

Whitening CNN-based rotor system fault diagnosis model features

Abstract Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models