This paper introduces an evaluated initial model for how product lifecycles can be extended considering new and emerging existential design aspects concerning both general as well as digital/connected products. The initial model, which is cyclic, includes reverse logistics of components and raw materials, as well as information on how to manage data at the end of lifecycles. The aim is to improve long-term sustainability with a high degree of circularity while also achieving increased profitability and competitiveness. Further, we highlighted that product providers must start to evaluate and prepare for how to improve product durability, manage long and extendable lifespans, and achieve circularity with reverse logistics to close the loops. Additionally, updatability and upgradability are also required to stay current with time and create value while being cybersecure. Otherwise, customers’ expectations, various legal and regulatory aspects, as well as other existential design aspects can halt or even terminate a product’s lifecycle.

The case study, spanning three contexts, concerns a multi-usable cloud service platform for big data collection and analytics and how the development pace and efficiency of it has been improved by 50–75% by using the Arrowhead framework and changing development processes/practices. Furthermore, additional results captured during the case study are related to technology, competencies and skills, organization, management, infrastructure, and service and support. A conclusion is that when offering a complex offer such as an Industrial Product-Service System, comprising sensors, hardware, communications, software, cloud service platform, etc., it is necessary that the technology, business model, business setup, and organization all go hand in hand during the development and later operation, as all ‘components’ are required for a successful result.

The paper addresses the need for a policy document in organizations concerned with Operational Technologies (OT) within their production and operational environments, and secondly how such an OT policy was developed and crafted by a Swedish municipality and its water production and wastewater management department. The first initial design criteria was to clearly distinguish the IT environment from the OT environment and the second design criteria was to achieve an improved, affordable and maintainable cybersecurity level for the OT environment. The results of the paper are an initial OT policy and an action plan for the necessary technical and organizational change in the OT environment.

This publication ‘Process-SME Project – Exceeded Expectations’ introduces a Nordic project Process-SME, which main objective is to improve the competitiveness of SMEs whose customers are found within the process, mining, energy, oil, and gas industries. The project supports these SMEs by identifying their needs and potential opportunities, developing new business models, building European partnerships and applying for EU-level funding for project proposals on SMEs’ business behalf. Furthermore, the Process-SME project aims to develop SMEs’ products, services and other offerings.

The paper addresses the first one-and-a-half cycles, out of four planned, in an action research effort concerned with predictive maintenance of walls and ceilings in tunnels of hard rock underground facilities by using Internet-of-Things-enabled Rock Bolts (IoTeRB). The IoTeRB concept is developed together with a consortium of companies ranging from rock bolt manufacturers, sensor specialists, researchers, and cloud-service providers to data analysts. Thus, the action research effort is a multi-disciplinary endeavor. The result of the paper is an action plan for the second cycle concerning technology and business development which, according to the design criterion, will move the IoTeRB toward commercialization.

The paper addresses intelligent and sustainable production achieved through combination and integration of online predictive maintenance, monitoring of process parameters and continuous quality control of both input materials and output from the process. This enables production systems, within both manufacturing and process industries, to move towards zero-defect manufacturing. Such a zero-defect manufacturing approach allows for earlier identification of problems or issues, which will or already negatively affect the output. The paper outlines the first part of the second cycle of an action research effort at Gestamp HardTech AB in Sweden, whose objective is to keep its position as a world-leading provider of press-hardened vehicle parts. In order to fully implement the zero-defect manufacturing approach, 4-6 action research cycles are expected to be needed in order to iteratively refine the approach. During the first cycle, various methods and solutions for some of the individual issues/problems have been conceptualized, realized and initially tested. The selected design criteria for the action research efforts were: simplicity, low cost, robustness, high-quality output and future-proofing. The result from the research in the second cycle so far is an action plan for the technical change and a set of challenges/problems which need additional investigation.

As more features are added to the heavy duty construction equipment, its complexity increases and early fault detection of certain components becomes more challenging due to too many fault codes generated when a failure occurs. Hence, the need to complement the present onboard diagnostic methods with more sophisticated diagnostic methods for adequate condition monitoring of the heavy duty construction equipment in order to improve uptime. Major components of the driveline (such as the gearbox, torque converter, bearings and axles) are such components. Failure of these major components of the driveline may results in the machine standing still until a repair is scheduled. In this paper, vibration based condition monitoring methods are presented with the purpose to provide a diagnostic framework possible to implement onboard for monitoring of critical driveline parts in order to reduce service cost and improve uptime. For the development of this diagnostic framework, sensor data from the gearbox, torque converter, bearings and axles are considered. Further, the feature extraction of the data collected has been carried out using adequate signal processing methods, which includes, Adaptive Line Enhancer, Order Power Spectrum respectively. In addition, Bayesian learning was utilized for adaptively learning of the extracted features for deviation detection. Bayesian learning is a powerful prediction method as it combines the prior information with knowlegde measured to make update. The results indicate that the vibration properties of the gearbox, torque converter, bearings and axle are relevant for early fault detection of the driveline. Furthermore, vibration provide information about the internal features of these components for detecting deviations from normal behavior.

In this way, the developed methods may be implemented onboard for the continuous monitoring of these critical driveline parts of the heavy duty construction equipment so that if their health starts to degrade a service and/or repair may be scheduled well in advance of a potential failure and in that way the downtime of a machine may be reduced and costly replacements and repairs avoided.

In a case study comprising two SMEs offering Industrial Product-Service Systems (IPS²) based on the industrial internet the paper addresses the current cybersecurity level of the two SMEs and the perceived need for added cybersecurity features. Cybersecurity is of crucial importance for most IPS²-offerings if they involve data communications, data collection and storage, and are also part of the customers’ critical processes (i.e., the core processes that always need to work with a high level of availability). The case study reveals that both IPS²-offerings have a basic level of core security spanning IoT-devices, IoT-networks, cloud services and users as well as administrators. Further, of interest is that the SMEs would like to add security cloud services with advanced security functionality in order to achieve scalability and efficiency regarding security- and general management as well as lifecycle management functionality. However, most of the security cloud services are mainly aimed at larger companies and not adapted for SMEs in terms of required knowledge, time and effort required to keep the security configurations up-to-date.

The paper addresses machine learning methods, utilizing data from industrial control systems, that are suitable for detecting anomalies in the press-hardening process of automotive components. The paper is based on a survey of methods for anomaly detection in various applications. Suitable methods for the press-hardening process are implemented and evaluated. The result shows that it is possible to implement machine learning for anomaly detection by non-machine learning experts utilizing readily available programming libraries/APIs. The three evaluated methods for anomaly detection in the press-hardening process all perform well, with the autoencoder neural network scoring highest in the evaluation.

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machinein combination with feature extraction and classification methods may be utilized.

This paper, based on a study at Volvo Construction Equipment,presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in a heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components,the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomalyis detected, the Case-Based diagnosis module is activated for fault severity estimation.