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Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox. This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data. [ABSTRACT FROM AUTHOR]

Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection. [ABSTRACT FROM AUTHOR]

• An adaptive signal analysis algorithm is developed for varying speed bearings and gearbox fault diagnosis. • The algorithm can effectively extract the defect signal components which energies are above the noise floor for an accurate fault diagnosis. The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under varying speed or/and load conditions. However, the time frequency representation (TFR) of a signal produced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to improve the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals. [ABSTRACT FROM AUTHOR]

• Experimental data from eccentric gears was successfully analyzed (unique and notable research investigation). • Feasibility of SIE with the aid of FFT, Wavelet transform, and Multi-Taper spectrum as a counterpart for tackling modulated data from gears was proven. • The use of PCA can significantly ease the reduction and visualization of high dimensionality machine data. • Proposed RF and SVM algorithms can efficiently classify gearboxes and overcome the similarity of the observations (unique investigation). The use of Condition Monitoring (CM) for engineering design, and industrial gears, has dramatically changed the gear design and manufacturing process in a short space of time. As CM deployment grows rapidly, the feasibility of CM for the diagnosis and prognosis of most common gear failure modes has well been investigated and documented. However, this is not the case for the CM of industrial gear eccentricity. Previous published work, in particular, focused only on the use of simulation models as a basis to investigate the gear eccentricity. Simulations cannot always ease the detection of complex problems and complications that are experienced in real operations. For instance, excessive eccentricity can be considered as one of manufacturing errors that may severely lead to direct effect on the overall dynamic performance of gears. Yet, it produces very high modulated mesh frequency rates; thus, making the detection of faulty machine components very difficult or even impossible. With this in mind, this paper presents the first known attempt at the diagnosis and prognosis of experimental vibration datasets from five different eccentric gear conditions. Datasets were first analysed using Signal Intensity Estimator (SIE) method in time and frequency domains. Then, the data was subjected to additional processing for the classification of gearbox status. Observations from the results showed that the proposed techniques could successfully discriminate the "good" and "bad" gears. [ABSTRACT FROM AUTHOR]

Abstract: Condition monitoring of gears with vibration analysis is well established whilst the application of acoustic emission (AE) to gear defect diagnosis and monitoring is still in its infancy. This paper details results of an experimental programme to ascertain and validate the applicability of AE to seeded gear defect identification. Furthermore, comparisons are made to vibration diagnosis. As a direct consequence of the experimental programme, the relationship between temperature, oil film thickness and AE activity were investigated. It is shown that similar to the lubricant film thickness between non-conforming surfaces under isothermal conditions, AE activity is not influenced by load. Limitations of applying AE to seeded defect identification are presented and it is concluded that the source of AE activity is attributed to asperity contact. [Copyright &y& Elsevier]

It is widely recognised that acoustic emission (AE) is gaining ground as a non-destructive technique (NDT) for health diagnosis on rotating machinery. The source of AE is attributed to the release of stored elastic energy that manifests itself in the form of elastic waves that propagate in all directions on the surface of a material. These detectable AE waves can provide useful information about the health condition of a machine. This paper reports on part of an ongoing experimental investigation on the application of AE for gear defect diagnosis. Furthermore, the possibility of monitoring gear defects from the bearing casing is examined. It is concluded that AE offers a complimentary tool for health monitoring of gears. [Copyright &y& Elsevier]

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emission (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. This has been achieved through developing of internal AE sensor for helicopter transmission system. In addition, series of signal processing procedure has been developed to improved detection of incipient damage. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission path. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three escalators gearboxes were diagnosed for pitting failure. [•] Vibration analysis was used for pitting detection. [•] Three algorithms were employed for signal analysis, including statistical metrics, Spectral Kurtosis and envelop analysis. [•] We find the pitting detection is depending on measurement direction. [Copyright &y& Elsevier]

Abstract: This paper reports an investigation into the use of Acoustic Emission (AE) for monitoring gear teeth defects under varying lubrication regimes in helical gears. The investigation used a back-to-back gearbox test-rig with oil-bath lubrication. Variation in oil film thickness was achieved by decreasing the gear metal temperature with nitrogen gas whilst the gears were in operation. Results demonstrate a clear relationship between AE activity, operating temperature and specific film thickness. In addition, results show that there are lubricating conditions that may prevent AE from identifying the presence of gear defects. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation into the detection of single bubble inception and burst with the Acoustic Emission (AE) technology. In addition, it presents results correlating the Gas Void Fractions in two phase gas–liquid flow with levels of AE activity. The findings demonstrate the feasibility of employing AE technology as an on-line monitoring tool for bubble detection and ascertaining flow patterns under two phase gas-liquid flow conditions. [Copyright &y& Elsevier]

Acoustic emission (AE) is gaining ground as a non-destructive technique for health diagnosis on rotating machinery. There are vast opportunities for development of the AE technique on various forms of rotating machinery, including gearboxes. This paper reviews some recent developments in application of AE to gear defect diagnosis. Furthermore, an experimental investigation that examines the effectiveness of AE for gear defect identification is presented. It is concluded that application of the AE technique to seeded gear defect detection is fraught with difficulties. In addition, the viability of the AE technique for gear defect detection from non-rotating components of a machine is called into question. [Copyright &y& Elsevier]

Condition monitoring of machine is recognized as effective strategy for undertaking the maintenance in wide variety of industries. Planetary gearbox is a critical component in helicopters, wind turbines, hybrid vehicles and so forth. Planetary gearbox are complex in nature due to its size and meshing components. Condition monitoring and fault diagnosis of planetary gearbox is challenging due to complexity in dependable fault extraction from raw vibration signal. The mechanism of planetary gearbox is complex as there are several gears meshing at the same time. To find out the nature of fault and defective component in planetary gearbox is difficult. In this paper, the fault detection and fault type identification diagnostic approach using auto regression model (AR) and continuous wavelet transforms (CWT) by considering different frequency range is established. The experimental research conducted with different type of fault vibration signals in the gearbox have been diagnosed and identified the fault type using AR Modelling, Impulse and Shape Factor for validation purposes. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. The fault frequency identification and extraction of features from the non-stationary signals in different fault severity level of vibration data demonstrates the reliability of proposed method. The developed algorithm adds efficacy in detecting the nature of fault and defective component without performing a visual inspection. [ABSTRACT FROM AUTHOR]

Machinery prognostics and health management (PHM) plays a key role in the reliable and efficient operation of industrial processes. With the emerging big data era, data-driven prognostic methods which avoid considering complicated system models have attracted growing research interest. Among many data-driven models, similarity-based prediction methods have been popular due to their strong interpretability and relatively simple implementation process. Nevertheless, when quantifying the similarity between two trajectories, most existing similarity measures neglect the nonlinearity of the distance measurement at different degradation stages and degradation alignments with timing difference, which may not be sufficient to retrieve the most suitable trajectories for remaining useful life (RUL) prediction. To overcome these limitations, a spherical-Dynamic Time Warping (spherical-DTW) algorithm is put forward to find an optimal match between the test and training trajectories at the retrieval step. Dynamic Time Warping allows degradation alignments with timing difference through stretching or compressing the trajectories with regard to time, thereby the data in similar degradation levels can be well aligned across different units. Moreover, a newly defined nonlinear spherical distance method is introduced and incorporated into the retrieval process to account for the nonlinearity of the damage propagation process. The significance of this study is that the newly proposed spherical-DTW algorithm goes one step further to consider the nonlinearity of fault evolutions and allow degradation pattern alignments with timing difference when performing similarity-based prognostics. Two run-to-failure cases, involving a real-world industrial compressor failure case and a gas turbine engine failure dataset, are investigated to demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Multi-information is introduced into a grey prediction model to replace the traditional single source information. • The robustness of a grey prediction model is highlighted. • The model's properties are investigated through systematic numerical simulations and case studies in wear trend prediction. • The result exhibits that the inclusion of multi-information can improve the accuracy and robustness of grey predictions. The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models. [ABSTRACT FROM AUTHOR]

Highlights • Benchmarking of prognostics methods on an instantaneous failure mode. • Applying prognostics on reciprocating compressor addressing lack of literature. • Use of actual, industrial, non-uniformly sampled valve failure measurements. • Proposal of KNNR based RUL estimation variation along with an ensemble method. • Proposal Hotelling T 2 and Q residuals metrics as health indicators and RUL predictors. Abstract Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create T 2 and Q metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques. [ABSTRACT FROM AUTHOR]

Abstract The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases. [ABSTRACT FROM AUTHOR]

• The development of a new monitoring index based on statistics T 2 , Q and T d. • The development of a CVR-based contribution method for the monitoring of slowly evolving faults. • Validated using data captured from a CSTR simulation program and an operational industrial centrifugal pump. The superior performance of canonical variate analysis (CVA) for fault detection has been demonstrated by a number of researchers using simulated and real industrial data. However, applications of CVA to fault identification of industrial processes, especially for faults that evolve slowly, are not widely reported. In order to improve the performance of traditional CVA-based methods to slowly developing faults, a novel diagnostic approach is put forward to implement incipient fault diagnosis for dynamic process monitoring. Traditional CVA fault detection approach is extended to form a new monitoring index based on indices, Hotelling's T 2 , Q and a canonical variate residuals (CVR)-based monitoring index T d. As an alternative to the traditional CVA-based contributions, a CVR-based contribution plot method is proposed based on Q and T d statistics. The proposed method is shown to facilitate fault detection by increasing the sensitivity to incipient faults, and aid fault identification by enhancing the contributions from fault-related variables and suppressing the contributions from fault-free variables. The CVR-based method has been demonstrated to outperform traditional CVA-based diagnostic methods for fault detection and identification when validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system and an industrial centrifugal pump. [ABSTRACT FROM AUTHOR]

Centrifugal compressors are widely used for gas lift, re-injection and transport in the oil and gas industry. Critical compressors that compress flammable gases and operate at high speeds are prioritized on maintenance lists to minimize safety risks and operational downtime hazards. Identifying incipient faults and predicting fault evolution for centrifugal compressors could improve plant safety and efficiency and reduce maintenance and operation costs. This study proposes a dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM). CVA was used to perform fault detection and identification based on the abnormalities in the canonical state and the residual space. In addition, CVA combined with LSTM was used to estimate the behavior of a system after the occurrence of a fault using data captured from the early stages of deterioration. The approach was evaluated using process data obtained from an operational industrial centrifugal compressor. The results show that the proposed method can effectively detect process abnormalities and perform multi-step-ahead prediction of the system’s behavior after the appearance of a fault. [ABSTRACT FROM AUTHOR]

The particle filter (PF) has been widely studied in the prognostics' field due to its ability to deal with nonlinear and non-stationary systems. However, there is no update of the model parameters during the prediction, preventing PF to work in its traditional way to generate accurate long-term predictions. In order to solve this problem, we put forward an improved PF that is based on a novel health index (HI) similarity matching method. This method is employed to search for similar HIs in the training library and construct an optimal "similar HI" for the system under study. Finally, the obtained HI is consistently fed into the PF to deliver precise state-of-health (SoH) estimates. The effectiveness of the proposed PF was validated on the C-MAPSS datasets as well as data collected from an operational reciprocating compressor. We observed that the new similarity matching method demonstrated excellent performance in finding suitable HIs for failure time prediction. We also observed that the proposed PF framework had a superior prognostics performance over the standard PF. We obtained an averaged predictive accuracy of 96% (C-MAPSS data) and 92% (compressor data) when only the first 10% of the degradation data were used. This work highlights the promise of combining index similarity, Procrustes analysis and PF for complementing existing prognostic methods. • Improves the predictive accuracy and uncertainty level of standard Particle filter. • This method realizes early/incipient fault prognosis. • We put forward a new Spherical-Cosine-distance-based similarity matching method. • We tested the proposed method on both simulation and real-world data. [ABSTRACT FROM AUTHOR]