Condition Indicators Research Articles

A Comparison of Acoustic Emission and Vibration Measurements for Condition Monitoring of an Offshore Drilling Machine

This paper investigates the application of heterodyned Acoustic Emission (AE) compared to more conventional vibration measurements for Condition Monitoring (CM) of an offshore drilling machine, with a particular focus on the large, axial tapered roller bearing supporting the drill string weight in a top drive. The focus on cost reduction and operational uptime in the oil and gas industry motivates research on improved CM methods for fault detection, identification and ultimately prediction. However, bearing failure on this type of machines are currently responsible for a significant share of operational downtime on drilling rigs. In the experiment, a previously used and replaced bearing is compared to a new, healthy bearing with the purpose of identifying possible condition indicators (CI) from the vibration and AE measurements. AE root-mean-square values (RMS) was identified as a CI, being more consistent with the expected bearing health than vibration measurements and also less affected by operating speed. The AE measurements also show complementary forced frequency identification capabilities compared to the vibration measurements. The particular failure mode with bearing roller end damage is described and seen in conjunction with the results.

An online hybrid prognostics ANFIS-PF method with an application to gearbox for RUL prediction

Rotary components are dealing with performance degradation phenomenon, which contains the message of unexpected damages. Therefore, prognostics and health management (PHM), has been introduced to calculate andpredict the remaining useful life (RUL) in order to prevent costly damages or repairs. Data-driven, model-based and hybrid-based techniques are three main categories of PHM techniques. From the health monitoring view, the main idea is to use the experimental run-to-failure data as an intelligence-based model for our gearbox and predict the RUL with the probability (model) based method (Particle Filtering). Firstly, to perform our prognostics technique, we require the degradation information from gearbox. Therefore in duration of 10 days, we conduct a run-to-failure experiment for a test bench with initiative fault injection in day 7th. Period of last-three-days is considered as run-to-failure signal for proposed algorithm. After preprocessing the data, we apply a combined prediction method ANFIS-PF, using Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Filtering (PF). ANFIS used as a prediction model tool, while the particle filter method was used to find a step-ahead behavior of the gear. ANFIS as a powerful data-driven method will model the prediction of degradation data and finally this model isapplied to particle filtering to predict a-step-ahead of the gear behavior until failure will happen. Meanwhile, some important signal characteristics known as condition indicators (CIs) have been extracted from the residual, energy, frequency based data processing. Then, the energy-based health index (HI) is calculated using threshold and sum of distributions, to show the degradation trend of tested gearbox. The online prediction results properlydemonstrate the performances of the proposed ANFIS-PF algorithm, to predict the RUL of gearbox system with a 95% confidence boundary distribution.

Online damage detection using recursive principal component analysis and recursive condition indicators

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using recursive principal component analysis (RPCA) in conjunction with online damage indicators is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal modes in online using the rank-one perturbation method, and subsequently utilized to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/nonlinear-states that indicate damage. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. An online condition indicator (CI) based on the L2 norm of the error between actual response and the response projected using recursive eigenvector matrix updates over successive iterations is proposed. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data. The proposed CI, named recursive residual error, is also adopted for simultaneous spatio–temporal damage detection. Numerical simulations performed on five-degree of freedom nonlinear system under white noise and El Centro excitations, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. Successful results obtained from practical case studies involving experiments performed on a cantilever beam subjected to earthquake excitation, for full sensors and underdetermined cases; and data from recorded responses of the UCLA Factor building (full data and its subset) demonstrate the efficacy of the proposed methodology as an ideal candidate for real-time, reference free structural health monitoring.

Improving condition indicators for helicopter health and usage monitoring systems

Purpose – The purpose of this paper is to suggest new method for improving the condition indicators (CIs) used in health and usage monitoring system based on signal separation of gears. Design/methodology/approach – The research method is based on employing signal separation techniques to improve gears signal and fault signature. The signal separation is based on adaptive filters concept. Findings – CIs estimated for the deterministic part of vibration signal show higher sensitivity to gears faults in comparison to indicators estimated based on the original signal. This method proposed could enhance early fault detection in gears, particularly for those applications where strong background noise from other sources in the machine masks the characteristics fault components. Originality/value – The contribution of this research is improving the CIs currently used for helicopter gearboxes. As consequence the safe operation and availability will be improved.

Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

Fault diagnosis of gearbox at an early stage is very important to avoid catastrophic failures. Condition indicators (CI) are used to quantify the level of vibration generated by the defected gears. A comprehensive comparison of various CIs, i.e., RMS, kurtosis, crest factor, FM0, FM4, M6A, NB4, energy ratio, NA4, energy operator and two new proposed condition indicators (PS-I and PS-II), have been performed for no crack, initial crack and advanced crack for different fluctuating profiles of input speed. This comparative analysis shows the responsiveness of indicators towards crack detection. Limited speed fluctuations and instantaneous high speed fluctuations are considered. Results suggest that newly proposed condition indicators are more robust, stable and effective towards crack detection under fluctuating profiles of speed. A modified time synchronous averaging (MTSA) is also proposed to increase signal-to-noise ratio (SNR).

The Development of a Diagnostic Model for the Deterioration of External Wall Tiles of Aged Buildings in Taiwan

This study creates a deterioration diagnostic model for the external wall tiles of aged buildings, using both stage 1 and stage 2 diagnostic methods. The visual test results are categorized based on their impact on public safety, and renovation strategies are proposed. The stage 1 diagnosis mainly adopts the D.E.R. visual inspection the deterioration assessment method. After producing an external wall tile Condition Indicator (CI) for the stage 1 diagnostic results that fall in a gray area, this research performed a stage 2 diagnosis using a tap tone test, followed by a fast Fourier transform and pattern recognition to analyze the tapping results. Finally, this study provides deterioration evaluation criteria for external wall tile replacement recommendations and a standard operating procedure for the deterioration diagnosis. This study also suggest directions for the future amendment of regulations and provide a basis of references for the government in determining urban renewal, renovation and maintenance strategies.

Low speed bearing fault diagnosis using acoustic emission sensors

In this paper, a new methodology for low speed bearing fault diagnosis is presented. This acoustic emission (AE) based technique starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies. Then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage. The resampling approach is able to segment the AE signal according to shaft crossing times such that an even number of data points are available to compute a single spectral average which is used to extract features and evaluate numerous condition indicators (CIs) for bearing fault diagnosis. Unlike existing averaging based noise reduction approaches that require the computation of multiple averages for each bearing fault type, the presented approach computes only one average for all bearing fault types. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The results in this paper have shown that the low sampled AE signals in combination with the presented approach can be utilized to effectively extract condition indicators to diagnose all four bearing fault types at multiple low shaft speeds below 10Hz.

Planetary gearbox fault diagnostic method using acoustic emission sensors

In this study, a new acoustic emission (AE) sensor-based planetary gearbox (PGB) fault diagnosis method is presented. The method includes a heterodyne-based AE data acquisition system, empirical mode decomposition (EMD)-based AE signal analysis method, and computation of condition indicators (CIs) for PGB fault diagnosis. The heterodyne technique is hardware-implemented to downshift the sampling frequency of AE signals at a rate compatible to vibration analysis. The sampled AE signals are processed using EMD to extract PGB fault features and compute the CIs. The CIs are input into supervised learning algorithms for PGB fault diagnosis. The method is validated on a set of seeded localised faults on all gears: sun gear, planetary gear, and ring gear. The validation results have shown a promising PGB fault diagnostic performance using the presented method.

Detection of on-aircraft fatigue cracks in a UH-60A planet carrier using spectral averaging

In this paper, a new method for planet carrier fatigue crack detection is presented. This method begins by time synchronously resampling vibration signals to segment the signals according to shaft crossing times, such that an even number of data points are obtained to compute a spectral average. Then, the result is used to extract features and evaluate numerous condition indicators (CIs) for planet carrier fatigue crack detection. The methodology is validated using data from a number of US Army UH-60A Black Hawk helicopter main transmissions. The results are presented using vibration measurements of faulted and unfaulted transmissions over low-torque levels in on-aircraft conditions

Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs) are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI) through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.

A New Spectral Average-Based Bearing Fault Diagnostic Approach

The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.

Indirect Condition Assessment of Water Mains

The knowledge of the technical condition of the water distribution systems (WDS) is a key issue to predict the performance of the WDS and to optimize maintenance and rehabilitation of the WDS. Common practice employs only practitioner experience to assess the technical condition of the WDS. The presented paper summarizes a basic theory of indirect condition assessment including several definitions of condition assessment, factors contributing to deterioration and possible outputs of the condition assessment. Furthermore, the legal requirements concerning this issue specifically in the Czech Republic and the Slovak Republic are listed. There is also a summary of the studies of condition assessment methods of water mains.As a part of the intended complex methodology for condition assessment of WDS, a methodology for condition assessment of water mains was developed. The methodology is based on condition indicators (CI) formulated using a multi-objective optimization. The paper presents the proposed methodology and its application in two case studies. The proposed methodology achieved good results and it showed certain significant benefits – e.g. satisfactory level of detail, optional user modification.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.This study reveals the most relevant features to be used for damage detection and condition monitoring of the gear system. It is also shown that the proposed procedure using a combination of cyclostationarity and autoregressive modeling enhance the ability to detect and diagnose mechanical wear in multi-staged planetary gears.

Amplitude of Probability Density Function (APDF) of Vibration Response as a Robust Tool for Gearbox Diagnosis

Abstract: A ‘Go’ or ‘No Go’ assessment is a safety requirement for quick and robust estimation of the condition of gearboxes used in helicopters and other critical machines. A range of vibration‐based condition indicators (CIs) has been developed to meet this requirement. CIs are compared automatically with pre‐set threshold values representing a healthy system, so that the health of the gearbox can be assessed and diagnosis made where necessary. The use of kurtosis of the residual signal of the measured vibration data, computed as part of the ‘FM4’ method, is widespread, because it is accepted as a good and reliable indicator. However, it has been observed in some cases that FM4 may not show a continually increasing trend with the propagation of a fault. This behaviour may lead to improper assessment of the severity of the fault. Hence, a new CI, based on the deviation in the normal probability density function (PDF) of the measured vibration data, is suggested which demonstrates an increasing trend that is more robustly and monotonically correlated with the fault propagation.

Normalised Root Mean Square and Amplitude of Sidebands of Vibration Response as Tools for Gearbox Diagnosis

Abstract: Quick assessment of the condition of gearboxes used in helicopters is a safety requirement. One of the most widely used helicopter on‐board‐mounted condition monitoring system these days is the Health and Usage Monitoring System. It has been specifically designed to monitor the condition of all safety‐critical components operating in the helicopter through calculation of so‐called condition indicators (CIs) – signal processing routines designed to output a single number that represents the condition of the monitored component. Among number of available parameters, there is a couple of CIs that over the years of testing have earned a reputation of being the most reliable measures of the gear tooth condition. At the same time, however, it has been observed that in some cases, those techniques do not properly indicate the deteriorating condition with the propagation of a gear tooth fault with the period of operation. Hence, three more robust methods have been suggested, which are discussed in this article.

Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

Plastic bearings are widely used in medical applications, food processing industries, and semiconductor industries. However, no research on plastic bearing fault diagnostics using vibration sensors has been reported. In this paper, a two-step data mining-based approach for plastic bearing fault diagnostics using vibration sensors is presented. The two-step approach utilizes envelope analysis and empirical mode decomposition (EMD) to preprocess vibration signals and extract frequency domain and time domain fault features as condition indicators (CIs) for plastic bearing fault diagnosis. In the first step, the frequency domain CIs are used by a statistical classification model to identify bearing outer race faults. In the second step, the time domain CIs extracted using EMD are developed to build a k-nearest neighbor algorithm-based fault classifier to identify other types of bearing faults. Seeded fault tests on plastic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and real vibration signals are collected. The effectiveness of the presented fault diagnostic approach is validated using the plastic bearing seeded fault testing data.

Data Mining Based Full Ceramic Bearing Fault Diagnostic System Using AE Sensors

Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data.

Gear Health Threshold Setting Based On a Probability of False Alarm

There is no established threshold or limit for gear vibration based condition indicators (CI) that indicates when a gear is in need of maintenance. The best we can do is set CI thresholds statistically, based on some small probability of false alarm. Further, to the best of our knowledge, there is no single CI that is sensitive to every failure mode of a gear. This suggests that any condition based maintenance system for gears will have some form of sensor fusion.Three statistical models were developed to define a gear health indicator (HI) as a function of CI: order statistics (max of n CIs), sum of CIs and normalized energy. Since CIs tend to be correlated, a whitening process was developed to ensure the HI threshold is consistent with a defined probability of false alarm. These models were developed for CIs with Gaussian or Rayleigh (skewed) distributions. Finally, these functions, used to generate HIs, were tested on gear test stand data and their performance evaluated as compared to the end state of the gear (e.g. photos of damage). Results show the HIs performed well detecting pitting damage to gears.

Condition Indicators for Gearbox Condition Monitoring Systems

Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.

49 Variable age of onset in an alzheimer family with a His163Tyr mutation in the presenilin 1 gene

Fault diagnosis of gearbox at an early stage is very important to avoid catastrophic failures. Condition indicators (CI) are used to quantify the level of vibration generated by the defected gears. A comprehensive comparison of various CIs, i.e., RMS, kurtosis, crest factor, FM0, FM4, M6A, NB4, energy ratio, NA4, energy operator and two new proposed condition indicators (PS-I and PS-II), have been performed for no crack, initial crack and advanced crack for different fluctuating profiles of input speed. This comparative analysis shows the responsiveness of indicators towards crack detection. Limited speed fluctuations and instantaneous high speed fluctuations are considered. Results suggest that newly proposed condition indicators are more robust, stable and effective towards crack detection under fluctuating profiles of speed. A modified time synchronous averaging (MTSA) is also proposed to increase signal-to-noise ratio (SNR).