Errol Zalmijn – Applying Causal Analytics for ASML Diagnostics: Results and Challenges (June 30, 2021)

Semiconductor lithography system issues are challenging to diagnose from predefined models and historic data alone. That’s because such systems are characterized by high-dimensionality, non-stationarity and nonlinear behavior across multiple time and spatial scales. One line of research at ASML is to investigate model-independent approaches, which can help to find previously unknown causal relations from system diagnostic data. Therefore, we combine information theory based transfer entropy with eigencentrality in time series analysis. However, this approach must be computationally efficient for ASML system diagnostics (and prognostics) in real time. Moreover, it must be able to locate unobserved variables and capture unique as well as joint causal influences. This presentation will discuss the merits and challenges of causal inference using information-theoretical concepts within ASML context and demonstrate a number of cases.

Bart Pollman & Dr. Wieger Tiddens – Smart use of sensor data leads to modern maintenance support in future ships (June 2, 2021)

The Royal Netherlands Navy is planning to introduce several new ship classes within the next decade. The new ships will be technologically much more advanced than the ships currently in service. At the same time the operational availability needs to increase and the crew size needs to get smaller. This combination leads to the necessity not only to automate many functions on board, but also to increase the level of support to the maintenance organization. Sensor data can be used to better predict failures of machinery in order to allow the maintenance organization to perform timely repair actions and prevent catastrophic failures. The developments include the reuse of available data used for Monitoring & Control purposes, the introduction of extra sensor technology to better detect failure modes that cannot be detected with current systems, and the introduction of AI and Machine Learning. The Royal Netherlands Navy is cooperating with Industry to ensure the timely availability of these new methods and technologies.

PDEng Frank Grooteman – Probabilistic analyses and its application to life predictions of aircraft structures (May 12, 2021)

Many aircraft are designed according to the deterministic damage tolerance philosophy to predict the crack growth life of the structure. Alternatively, a probabilistic damage tolerance analysis can be performed, called a structural risk analysis (SRA), taking into account all important scatter sources, such as, the initial flaw size, the inspection quality, the inspection scheme, the variability in loads and crack growth material properties, instead of using scatter and safety factors. For new military aircraft, SRA is mandatory. For current military aircraft, it already has become a valuable tool for fleet management, since it offers a risk (probability of failure) development over time, which cannot be obtained from the traditional deterministic damage tolerance analysis. By this, it better signals fleet management when to take corrective (maintenance) actions to prevent (critical) failures of the aircraft. This presentation will address the general concept of probabilistic analyses and its more specific implementation in the field of probabilistic fracture mechanics (SRA) using available fleet data. The approach will be supported by a number of examples.

Prof. John Andrews – Next Generation Prediction Methodologies and Tools for Engineering Risk Assessment (April 7, 2021)

Risk Assessments performed on systems across many industrial sectors employ techniques such as Fault Tree Analysis and Event tree analysis which have their foundations back in the 1960/1970s. Since that time technology has advanced and system designs, their operating practice and maintenance strategies are now significantly different to those of the 1970s. Some of the restrictive assumptions such as: constant failure and repair rates for components, component failures being independent and the limited account of maintenance and renewal options in the component failure models employed, reduce the effectiveness of these methodologies to represent modern day systems. In addition, research into the risk prediction techniques has made considerable advances in their capabilities since the 1970s but these advances tend to have addressed each deficiency in isolation.

Prof. Pierre Dersin – Characterizing RUL loss rate (March 3, 2021)

The “RUL loss rate”, or time derivative or the RUL (remaining useful life), measures the speed at which an asset’s condition degrades and it therefore is getting closer to failure in the absence of any preventive maintenance action. The average RUL loss rate is the derivative of the “mean residual life” (MRL) ; therefore understanding the latter’s properties is of potential interest for maintenance policy optimization. First we study a special class of time-to-failure distributions: those for which the MRL is a linear function of time, i.e. the average RUL loss rate is constant.

Prof. Olga Fink – Domain adaptation and hybrid algorithms for intelligent maintenance systems Speaker (Feb 3, 2021)

The amount of measured and collected condition monitoring data for complex industrial assets has been recently increasing significantly due to falling costs, improved technology, and increased reliability of sensors and data transmission. However, faults in safety critical systems are rare. The diversity of the fault types and operating conditions makes it often impossible to extract and learn the fault patterns of all the possible fault types affecting a system. Consequently, faulty conditions cannot be used to learn patterns from. Even collecting a representative dataset with all possible operating conditions can be a challenging task since the systems experience a high variability of operating conditions. Therefore, training samples captured over limited time periods may not be representative for the entire operating profile. The collection of a representative dataset may delay the implementation of data-​driven fault detection and isolation systems.

Dr. Alessandro Di Bucchianico – Statistical Process Control and Predictive Maintenance (Jan 20, 2021)

SPC (Statistical Process Control) is the part of industrial statistics that deals with monitoring data streams in order to timely detect changes (the word control in the name is a historical misnomer because it leads to confusion with feedback control; a better name should have been Statistical Process Monitoring). The field originated in the manufacturing industry. In this talk, we give a brief overview of the standard procedures in SPC, applications fields and position SPC in the wider data science context. We will discuss an industrial case study with wind turbines, that illustrates the methodological challenges in the field. The wind turbine case study arose in a maintenance context. We will discuss benefits of including SPC in predictive maintenance strategies and highlight some methodological challenges and how they could fit within the PrimaVera context.

Dr. Ayse Sena Eruguz – Maintenance Optimization for Multi-Component Systems with a Single Sensor (Dec 9, 2020)

We consider a multi-component system in which a condition parameter is monitored by a single sensor. Monitoring gives the decision maker some information about the system state, but it does not reveal the exact state of the components. The decision maker infers a belief about the exact state from the current condition signal and the past data, in order to decide when to intervene for maintenance. A maintenance intervention consists of a complete and perfect inspection followed by component replacement decisions. We model this problem as a partially observable Markov decision process. We consider a deterioration process that suitably reflects the deterioration characteristics of a multi-component system and a probabilistic relation between system states and condition signals. Under reasonable conditions, we investigate the structure of the optimal maintenance intervention policy.