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Solid-oxide fuel cells (SOFCs) represent a group of electrochemical conversion devices that utilise hydrogen rich fuels and are characterised by their high efficiency of energy conversion. For optimal exploitation of SOFCs, accurate and online state-of-health (SoH) assessment is of utmost importance. SoH assessment is usually performed through the frequency domain analysis by characterising the changes of the Nyqvist curves, a process also known as electrochemical impedance spectroscopy. Such an approach is time consuming and suffers from low accuracy particularly at low frequencies. Methodologies for time-domain identification of fractional-order systems offer a way of resolving these issues. Using an algebraic approach to identification, the complete fractional-order transfer function is identified from a series of several step responses. The SoH can be estimated from the identified parameters of the fractional-order transfer function either in a form of a Nyqvist curve or by directly linking particular parameter values to electrochemical processes. The proposed approach was validated on a 300 W SOFC using pure hydrogen fuel.

Operation of the SOFC power system can be improved by upgrading the low-level controllers with a supervisory controller (see Figure 1). In this work a simple, model-free supervisory optimizer that adjusts references for the low level controllers is proposed. The optimization problem for the SOFC power system is formulated with the cost function depending on the electrical efficiency of the system and deviations of the system temperatures from their limits. Electrical efficiency is calculated as the ratio of the net electrical power to the total power obtained by burning the fuel. The dominant parasitic losses of the system are assumed to be mainly related to the air blower consumption. Based on efficiency analysis, which demands a number of simulations on SOFC power system model, the references for the burner lambda and stack outlet and inlet air temperatures are selected as optimization variables. The optimization problem is solved by using the extremum-seeking approach where optimum is sought directly on the process. An advantage of this approach is that it does not need a model of the system and exceeds the weakness of model-based optimizers, which assume nominal process condition all the time and do not take into account the degradation processes in fuel cells. Its disadvantage is it needs to probe the system at all time by imposing small variations on manipulated variables and delivers a convergence speed which is somewhat slower compared to the model based approach. A simple algorithm is designed to perform the model-free optimization. The algorithm is additionally augmented to incorporate changes in the current load in order to increase the convergence speed of the optimization at large load changes. The efficiency of the optimizer is governed by properly tuned parameters of the optimization algorithm. Proposed supervisory optimizer is assessed on a simulated detailed physical model of a 2.5 kW SOFC power system by applying large step current load changes. Simulation results indicate that the optimizer improves efficiency of the system by approximately 10 % compared to the low-level controllers and keeps the system temperatures within the safe ranges (see Figure 2). Due to its simplicity the optimizer appears appropriate for practical applications. Figure 1

Having concurrent information regarding the state of health (SoH) of an operating solid oxide fuel cell (SOFC) stack can actively improve its overall management. Firstly, operating the SOFC by taking into account current health conditions, and its anticipated trend, can be beneficial to the total life span of the stack. Furthermore, such information can be of great importance to the maintenance staff, e.g. unplanned shutdowns can be avoided. Relatively little work has been done in the field of remaining useful life (RUL) prediction of SOFCs. The majority of work employs stack/cell voltage as a direct link for RUL predictions. This paper proposes an integrated approach for SoH estimation based on stack’s Ohmic area specific resistance (ASR). Subsequently, a drift model that describes the ASR increase over time enables accurate RUL prediction. The approach consists of three steps. Firstly, an Unscented Kalman filter, based on a lumped stack model, estimates the current ASR value. Secondly, a drift model for describing the temporal evolution in ASR is recursively identified employing the linear Kalman filter. Finally, employing the identified drift model, Monte Carlo simulation is performed to predict future time evolution in ASR and so to obtain RUL. The developed approach is validated with experimental data from a 10 kW SOFC power system. The results confirm that ASR is a viable SoH indicator for the SOFC stack.

Degradation and poisoning of solid oxide fuel cell (SOFC) stacks are continuously shortening the lifespan of SOFC systems. Poisoning mechanisms, such as carbon deposition, form a coating layer, hence rapidly decreasing the efficiency of the fuel cells. Gas composition of inlet gases is known to have great impact on the rate of coke formation. Therefore, monitoring of these variables can be of great benefit for overall management of SOFCs. Although measuring the gas composition of the gas stream is feasible, it is too costly for commercial applications. This paper proposes three distinct approaches for the design of gas composition estimators of an SOFC system in anode off-gas recycle configuration which are (i.) accurate, and (ii.) easy to implement on a programmable logic controller. Firstly, a classical approach is briefly revisited and problems related to implementation complexity are discussed. Secondly, the model is simplified and adapted for easy implementation. Further, an alternative data-driven approach for gas composition estimation is developed. Finally, a hybrid estimator employing experimental data and 1st-principles is proposed. Despite the structural simplicity of the estimators, the experimental validation shows a high precision for all of the approaches. Experimental validation is performed on a 10 kW SOFC system.

Accurate prediction of the remaining useful life (RUL) of machines is becoming mandatory in exploiting the asset in an efficient and secure way by avoiding the unplanned downtimes. In this paper we present an approach to the RUL prediction developed for a shot blasting machine by analyzing the recordings from inexpensive vibrational sensors. The key idea consists of (i) employing generalized Jensen-Renyi divergence (JRD) as a measure of change in the vibrational pattern (ii) exploiting the monotone relationship between JRD and the abrasive wear in rotor blades and (iii) using a Markov model to describe wear dynamics. The unknown parameters of the Markov model are obtained by expectation-maximization approach. Prediction of the remaining useful life is done by executing Monte Carlo simulations on the updated model and evaluation of the first passage time of the JRD index. The approach is validated experimentally by running the machine up to the failure, hence allowing for naturally evolving wear progression.

Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

Standard approaches to the diagnostic decision making require prior knowledge about the diagnosed process and data records at various faulty conditions. This is not easy to meet in practice. In this paper we propose a data-driven non-parametric approach to fault detection and isolation, which relies on evaluating the dissimilarity statistic feature evaluated from current measurement records and features taken during the nominal fault-free machine condition. For that purpose the Jensen-Renyi divergence of the empirical distributions is employed. Hence the need for data records at different failure modes is avoided. The approach has been initially developed for robust bearing diagnostics and is presented here in that context. It has been validated on simulated and experimental case studies with bearings faults. Moreover, the approach is rather general and applicable in a number of other domains as well.

Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option. In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3% error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling. The approach is experimentally validated on a 10 kW SOFC system.

In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.