**3.3. Product support development: design for maintainability**

Maintainability is characterised as the ease of retaining or restoring a product in effective use conditions by using specific procedures and resources [24]. It is an important factor in the economic success of an engineering system. "Design for maintainability requires an evaluation of the accessibility and reparability of the inherent systems and their related equipment in the event of failure, as well as of integrated systems shutdown during planned maintenance" [25]. Maintainability procedures and techniques not only avoid and fix failures they also consider how a system might fail. Three types of maintenance can be distinguished: breakdown maintenance (corrective maintenance), preventive maintenance, and predictive maintenance (condition-based maintenance).

To help deliver these benefits the company creates two descriptions for monitoring and new product development in addition to the PLM descriptions mentioned in Section 2 for product,

**i.** Current product governance—during product in operation/ field data to help new prod-

**ii.** Product support development—during product development to help product monitoring.

In the diesel engine company, the 'voice of the customer' (VOC) is captured in many ways: directly, through discussions, interviews and workshops with customers, and indirectly through analysing customer specifications, warranty data, and field reports etc. and through dealers and distributor channels. Quality Functional Deployment (QFD) is applied to identify critical techni-

The company uses Failure Modes and Effects Analysis (FMEA) to evaluate a potential design for possible failures and to prevent them by proactively changing the design rather than reacting to adverse events after failures have occurred. This emphasis on prevention may reduce risk of failure in field. FMEA is particularly useful in evaluating a New Product Introduction programme prior to implementation as well as in assessing the impact of a proposed change to an existing design. More details about FMEA and steps of FMEA analysis can be found in [21]. FMEA is one of the most widespread methods used in determining priorities for techni-

To identify the potential effects, the company reviews documents, including historical data, warranty documents, field service data, and customers' complaints. The company rates the severity of the effects of a failure mode. Any failure occurring in the field is considered as a high risk. Issues identified in use significantly drive next generation product development and testing procedures. The company continuously monitors and captures a product's performance and durability when engines are used in a field. For a new product development, the company uses information from the 'use in the field' to assess how the product is performing and from the 'use of the customer' (how customers are using the product) to judge when

Field data is particularly valuable as it consists of information about failures and repair actions that have been taken place under real operating conditions. This enables the acquisition of statistically significant reliability and repair data [23]. Issues in recording field incidents are addressed by Smith [23] particularly how reliance on people means that recording is subject

Maintainability is characterised as the ease of retaining or restoring a product in effective use conditions by using specific procedures and resources [24]. It is an important factor in the economic success of an engineering system. "Design for maintainability requires an evaluation

cal requirements of the design which will need verification and validation by testing.

**3.2. Current product governance: field data and new products**

cal risks in the PD process especially during the testing phase [22].

design and performance:

84 Product Lifecycle Management - Terminology and Applications

uct development,

a potential failure is likely to occur.

to errors, omissions and misinterpretation.

**3.3. Product support development: design for maintainability**

Condition monitoring and fault diagnosis techniques are used for predictive maintenance [26]. Product health monitoring is a research area that covers failure detection, current health assessment and remaining useful life prediction [26, 27]. According to Fu et al. [28], most failures do not occur instantaneously. There is degradation and associated symptoms before the actual failure. The main objective of the predictive maintenance is to reliably identify these degradation processes so that maintenance can be affected before the actual breakdown. Predictive maintenance is based on the product's performance and condition monitoring data. For example, in well-established methods, vibration data is analysed to find the frequency responses to identify the type of fault present in the equipment [27].

At the design and development design stage the main characteristics of a product are determined and product performance is evaluated. Therefore, design for maintainability should be considered during the product development. However, according to Coulibaly et al. [29], there is lack of an efficient tool for considering maintainability and serviceability at the early design. Also, there is limited research on how information from design, CAE and tests can support product maintenance.

Kiritsis et al. [30] have commented that clear definition of the information for maintenance is required if appropriate and adequate information is to be collected. Usually, data collected during Middle of Life (MOL) phase of product is for maintenance management purposes and may not be appropriate for feeding back to the Beginning of Life (BOL) phase to redesign or improve a product. Although people involved in this process often have a clear understanding of the required information, it is not straightforward to define or determine exactly what information will be required.

A baseline performance description would allow degradation over a period of use to be assessed. As mentioned before, advanced engineering products such as the diesel engines studied here are equipped with instruments such as sensors, meters, controllers, and computational devices and have the processing capacity to self-detect/ predict certain problems. Next section proposes a conceptual model to facilitate this process. Design and testing data from the EOL stages can be a useful reference point for comparing with monitoring data for predictive maintenance. Also, this model can help to clearly define the information required to be collected to comparison.
