Tiedo Tinga

Application of physical failure models to enable usage and load based maintenance

The efficiency of a preventive maintenance process largely depends on the ability to predict the replacement intervals of components. Considering the actual usage of the system increases the accuracy of this prediction. The present paper proposes two new maintenance concepts, that combine the benefits of traditional static concepts and condition based maintenance. These new concepts, usage based maintenance and load based maintenance, apply usage or load parameters that are monitored during service to perform a physical model-based assessment of the system condition. The new concepts are positioned within the range of existing maintenance concepts. Also, the role of physical models in maintenance modelling in general is explained and the origin of uncertainty in the predicted service life is discussed. Moreover, it is demonstrated how the monitoring of usage, loads or condition can reduce this uncertainty and increase the service life, by extending existing work in this field. Finally, the different concepts are applied to a gas turbine blade case study to illustrate the benefits of the proposed concepts.

The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance

Recent developments in condition monitoring technology have led to an ongoing shift from time-based maintenance (TBM) to condition-based maintenance (CBM). Although CBM allows for more effectively planned maintenance actions, its relative performance strongly depends on the behavior of the deterioration process, the severity of failures, the required setup time, the accuracy of the condition measurements, and the amount of randomness in the deterioration level at which failure occurs. The contribution of this paper is twofold. First, we review studies that compare CBM with TBM, and studies that consider the above factors in combination with a CBM model. Second, whereas existing studies confine themselves to a few examples, we perform a numerical investigation to derive insights on the effects of the various characteristics on the relative benefit of CBM. The results can be used by companies to decide what factors are most important when considering to implement CBM, and to assess whether the benefit of CBM during the operational phase outweighs the additional costs during the life cycle of equipment. This study allows for follow-up research to quantify and generalize the insights obtained, and to analyze interaction effects.

Reducing costs by clustering maintenance activities for multiple critical units

Advances in sensor technology have enabled companies to make significant progress towards achieving condition-based maintenance (CBM). CBM provides the opportunity to perform maintenance actions more effectively. However, scheduling maintenance at the unit level may imply a high maintenance frequency at the asset level, which can be costly and undesirable for safety reasons. In this paper, we consider systems consisting of multiple critical units for which a strict and conservative maintenance strategy is enforced. Although this implies that benefits cannot be obtained by delaying maintenance activities, the clustering of them can be beneficial. We consider two simple, practical systems for condition monitoring that involve either one signal (alarm) or two signals (alert, alarm). Our analysis and results provide general insights into when and how to cluster maintenance operations, with the objective of minimizing the total maintenance costs. Moreover, they show that clustering is essential for a broad range of circumstances, including those at a considered real-life case of equipment maintenance at Europe׳s largest gas field.

Wireless sensor network for helicopter rotor blade vibration monitoring: Requirements definition and technological aspects

The main rotor accounts for the largest vibration source for helicopter fuselage and components. However, accurate blade monitoring has been limited due to the practical restrictions on instrumenting rotating blades. The use of Wireless Sensor Networks (WSNs) for real time vibration monitoring promises to deliver a significant contribution to rotor performance monitoring and blade damage identification. This paper discusses the main technological challenges for wireless sensor networks for vibration monitoring on helicopter rotor blades. The first part introduces the context of vibration monitoring on helicopters. Secondly, an overview of the main failure modes for rotor and blades is presented. Based on the requirements for failure modes monitoring, a proposition for a multipurpose sensor network is presented. The network aims to monitor rotor performance, blade integrity and damage monitoring at three different scales referred to as macro layer, meso layer and micro layer. The final part presents the requirements for WSNs design in relation with sensing, processing, communication and actuation. Finally power supply aspects are discussed.

Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner

When designing a maintenance programme for a capital good, especially a new one, it is of key importance to accurately understand its failure behaviour. Failure mode and effects analysis (FMEA) and fault tree analysis (FTA) are two commonly used methods for failure analysis. FMEA is a bottom-up method that is less structured and requires more expert knowledge than FTA, which is a top-down method. Both methods are time-consuming when applied thoroughly, which is why in many cases, they are not applied at all. We propose a method in which both are used in a recursive manner: First, a system level FTA is performed, which results in a set of failure modes. Using FMEA, the criticality of the failure modes is assessed in order to select only the critical system level failure modes. For each of those, a function level FTA is performed, followed by an FMEA. Finally, a component level FTA and FMEA are performed on the critical function level failure modes. We apply our method to a recently developed additive manufacturing system for metal printing, the MetalFAB1 of Additive Industries (AI), and find that the engineers at AI consider the method to be efficient and effective.

Vibro-acoustic modulation–based damage identification in a composite skin–stiffener structure:

Vibro-acoustic modulation–based damage identification relies on the modulation of a high-frequency carrier signal by an intenser low-frequency vibration signal due to damage-induced structural nonlinearities. A time domain analysis of the vibro-acoustic modulation phenomena was presented at multiple spatial locations in an impact damaged composite skin–stiffener structure. The instantaneous amplitude and frequency of the carrier velocity response were extracted to analyze the intermodulation effects between the two excitation signals. Increased amplitude modulations at the damaged region revealed the presence, location, and length of the skin–stiffener damage. The damage hardly modulated the frequency of the carrier response. This difference in behavior was attributed to the nonlinear skin–stiffener interaction introduced by the periodic opening and closing of the damage, according to earlier research by authors on the same structure. A parametric study showed that the amplitude and phase of the amplitude modulation are dependent on the selected carrier excitation frequency, and hence the high-frequency wave field that is introduced. This work demonstrates not only the potential but also the complexity of the vibro-acoustic modulation based damage identification approach.

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these units to construct a network. Unsupervised learning is also carried out for each restricted Boltzmann machine layer in this procedure to compute the network weights and biases. Finally, the network output is fed into SFAM to perform fault classification. In order to diagnose the valve faults, three signal types of vibration, pressure, and current are acquired from a two-stage reciprocating air compressor under different valve conditions such as suction leakages, discharge leakages, spring deterioration, and their combination. These signals are subsequently processed so that the useful fault information from the signals can be revealed; next, statistical features in the time and frequency domains are extracted from the signals and used as the inputs for hybrid deep belief network. Performance of hybrid deep belief network in fault classification is compared with that of the original deep belief network and the deep belief network combined with generalized discriminant analysis, where softmax regression is used as a classifier for the latter two models. The results indicate that hybrid deep belief network is more capable of improving the diagnosis accuracy and is feasible in industrial applications.

Predictive maintenance of maritime systems : models and challenges

To reduce maintenance and logistic costs and increase the asset availability, a predictive maintenance concept for maritime systems is developed. In the present paper, the physics-of-failure based prognostic methods will be introduced, but also other issues related to the development and application of these models will be discussed. Typically, the following challenges are encountered in such a development trajectory: (i) critical part selection, (ii) predictive modelling (data-driven or physics based), (iii) monitoring / data collection, (iv) model validation and (v) making the business case. These challenges will be discussed us-ing two case studies: the cylinder liners of a diesel engine and printed circuit boards (PCB) in a radar system.

The business case for condition-based maintenance: a hybrid (non-) financial approach

Although developing business cases is key for evaluating project success, the costs and benefits of condition-based maintenance (CBM) implementations are often not explicitly defined and evaluated. Using the design science methodology, we developed a hybrid business case approach to help managers evaluate and justify implementing CBM. We conclude that depending on the innovativeness (for the organization) of the applied technique, the business case should have a different goal orientation and be composed of different support elements. We use the proposed hybrid business case approach in an in-depth single case study that focusses on developing engine condition trend monitoring for a military transport aircraft. The case study ex-plores differences in applying innovative maintenance techniques (exploration) or applying well-known tech-niques (exploitation). Using a combination of non-financial (strategic multi-criteria analysis) and financial ele-ments (using Monte Carlo simulation), we compared the investment in CBM with both fixed-interval preventive maintenance and corrective maintenance.