# Decision Framework for Predictive Maintenance Method Selection
## 3 Design of the Framework Elements
### Element 1: Classification of the Available Predictive Maintenance Approaches  
To be able to select a predictive maintenance approach, the various combinations of maintenance policies and MMs that are described in the literature have to be distinguished and classified first (problem 1). However, among reviewers within the diagnosis and prognostics field, little consensus exists as to which classification of methods is most appropriate [22]. In this paper, therefore, a broad classification is adopted to encompass the various views in this field and, at the same time, to be compatible with the different ambition levels (as will be presented later in the description of element 2). Thus note that the proposed classification is not claimed to be the best or only right structuring of the vast number of concepts and methods in this field, but merely the classification that fits the purpose of this paper best. Further note that all maintenance methods that are included here are considered _predictive maintenance_ methods. Strictly speaking, that might not be correct, although a generally accepted definition of predictive maintenance does not exist. However, to be able to optimise any preventive maintenance policy, an estimate or prediction of the time to failure is required. The methods considered here all provide such a prediction, albeit with different levels of accuracy. An expert giving an estimate of a component's lifetime based on many years of operational experience might not be considered to be part of predictive maintenance, but is in this work still included as a source of life 'prediction'. The case study demonstrated that this (least advanced) category is still the most used type of maintenance method in practice. In the proposed categorisation, the model proposed by [PERSON] and [PERSON] [32], which has already been extended by [PERSON] [33] with an additional category (V-model-based), is adopted. This model has been further extended [10] with the experience-based approach by differentiating between methods that employ historical records (data) and those that only employ expert knowledge and the experience of people who use and maintain the equipment [19]. The framework is thus proposed to contain five types of predictive maintenance approaches:  
1. **Experience-based predictions** of failure times are based on knowledge and previous experience outside (e.g., OEM) or within the company. Sometimes they are supported by limited or scattered data. Predictions are based on expert judgement (e.g., facilitated by failure mode, effect and criticality analysis (FMECA) techniques). These methods estimate the life of an average component operating under historically average conditions. 2. **Reliability Statistics prediction** techniques are based on historical (failure) records of comparable equipment without considering component-specific (usage) differences. This approach accurately describes population-wide failure probabilities. Examples are the reliability engineering or survival analysis [28] approaches and the analyses using fault trees, Markov chains or other stochastic processes [26; 27]. These methods also estimate the life of an average component operating under historically average conditions. 3. **Stressor-based predictions** are based on historical records supplemented with stressor data, e.g., temperature, humidity or speed, to include environmental and operational variabilities and give results in terms of the expected lifetime of an average system in a specific environment. Examples of this type of method are the hazard or failure rate models from reliability handbooks that contain multiplication factors for certain covariates or stressors. Predictions are typically based on collected data from build-in tests or operating history under different conditions, but the effect of stressors can also be derived from expert knowledge or (simple) physical models [23]. 4.