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Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

This study aims to use the database of consumers referred to the dual diagnosis shared care service to examine those connections. The Eastern Dual Diagnosis Service, based in Melbourne, Australia, has established a database of consumers with co-occurring mental health disorders and problematic substance use. An examination of mental health and substance-use information obtained over a two-year period in the delivery of dual diagnosis shared care to consumers of mental health services is supporting an improved understanding of substance use and the connections to specific mental health diagnoses of schizophrenia, bipolar disorder and schizoaffective disorder.

This research uses a quantitative approach to review the prevalence of primary substance use and mental health diagnoses for consumers referred to as dual diagnosis shared care. Reviewed are referrals from adult mental health community and rehabilitation teams operating within a mental health and well-being program between January 2019 and December 2020 inclusive.

Of the 387 clients referred to the specialist dual diagnosis shared care, methamphetamine, alcohol and cannabis are associated with 89.4% of the primary mental health diagnosis (PMHD). The most common PMHDs are schizophrenia, schizoaffective disorder and bipolar disorder. The most common PMHD and substance-use connection was schizophrenia and methamphetamine. Nicotine was reported to be used by 84% of consumers and often occurred in addition to another problematic primary substance.

Improved dual diagnosis data collection from a community-based clinical mental health service is increasing understanding of the mental health and substance-use relationship. This is now providing clarity on routes of investigation into co-occurring mental health and problematic substance-use trends and guiding improved integrated treatments within a contemporary mental health setting.

Autism spectrum conditions (ASC) and borderline personality disorder (BPD) have overlapping symptom profiles. Dialectical behaviour therapy (DBT) is an established treatment for self-harm and BPD, but little research has investigated the outcomes of DBT for ASC populations. This exploratory service evaluation aims to investigate the outcomes of a comprehensive DBT programme for adolescents with a diagnosis of emerging BPD and a co-occurring ASC diagnosis as compared to those without an ASC diagnosis.

Differences from the start to end of treatment in the frequency of self-harming behaviours, BPD symptoms, emotion dysregulation, depression, anxiety, the number of A&E attendances and inpatient bed days, education and work status, and treatment non-completion rates were analysed for those with an ASC diagnosis, and compared between those with an ASC diagnosis and those without.

Significant medium to large reductions in self-harming behaviours, BPD symptoms, emotion dysregulation and inpatient bed days were found for those with an ASC diagnosis by the end of treatment. There were no significant differences between those with an ASC and those without in any outcome or in non-completion rates. These findings indicate that DBT may be a useful treatment model for those with an ASC diagnosis, though all results are preliminary and require replication.

To the best of the authors’ knowledge, this is the first study to report the outcomes of a comprehensive DBT programme for adolescents with an ASC diagnosis, and to compare the changes in outcomes between those with a diagnosis and those without.

This study aims to examine the impact that diagnostic factors such as duration of diagnostic period, number of professionals consulted and perceived social support have on parental stress during the diagnostic process of autism spectrum disorder (ASD).

Forty parents of 2–18 year-old children/adolescents with a formal ASD diagnosis recruited from five specialized private clinics in Lisbon completed a survey which included diagnostic questionnaire, parental stress scale and social support scale from April to December 2022.

Data analysis indicated that the mean age of the diagnosis was 5.6 years with a delay of 3.95 years from first concern until final diagnosis. On average, parents consulted a mean of 3.62 professionals. Other findings indicated that as age of parents increased, levels of parental stress decreased. Parents with higher levels of social support had lower levels of parental stress. Furthermore, higher age of child at first concern predicted higher affective social support.

This study reflects on the experience of obtaining the ASD diagnosis in Portugal, raising awareness on the importance of providing early detection and social support for distressed parents.

Chapter 6 explores the cultural impact of mental health, addiction, and dual diagnosis challenges with a specific focus on Irish society. The chapter takes a staggered approach whereby each type of challenge is separated and discussed individually, before being joined together through discussions on dual diagnosis as presented in Section 6.4. These discussions are designed to highlight the cultural deviance that is perceived by other people towards those with such diagnosis over the years that such diagnosis have been prevalent in history. In particular, the discussions around dual diagnosis here will strengthen the discussion in Chapter 4: ‘The Conundrum of Dual Diagnosis’ and will set the groundwork for the remainder of this text.

The purpose of this paper is to explore the historic policy context and social implications of the diagnosis of personality disorder and also consider formulation-based and trauma-informed understandings of distress.

Ongoing changes to (and splits between) medical understandings of what is being labelled as personality disorder have eroded the label’s cultural capital, adding weight to lived-experience-led calls to Drop the Disorder (Watson, 2019). This paper explores the impact and implications of the historic policy and practice context through a lived experience lens.

Such diversity of views in the lived experience and medical communities on personality disorder has allowed alternatives to diagnostically informed understandings of distress (such as formulation-based and trauma-informed approaches) to gain traction with practitioners (Bloom and Farragher, 2013; Johnstone and Boyle, 2020). The broader assimilation of these alternative perspectives into dominant medical ideology is evidenced by the fact that the Royal College of Psychiatrists (RCP) is now also exploring alternatives to diagnosis (2023). This suggests even more change ahead for how we understand people and their relationships with trauma and distress.

This paper discusses UK policy and does not include broader global policies.

This paper would be helpful for any student interested in where the ideas that underpin personality disorder diagnosis stemmed from and why so many lived experience practitioners and experts by profession question the diagnosis' legitimacy.

As the RCP is now considering alternatives to diagnosis, it is even more critical that practitioners are aware of the competing narratives surrounding this contested diagnosis – as the author believes this will promote more compassionate, trauma-informed working practices.

This is the author’s own work and includes not only the RCP position change but also directly quotes Professor Tyrer (who wrote the International Classification of Diseases 11), giving his views on the changed RCP position, as he recently presented at a conference here in Cornwall. The author is a part of Lighthouse peer support group and wrote this paper as preparation for a Participatory Action Research project they are planning, where they will evaluate the Sanctuary Approach with their membership to create a lived experience-designed trauma-informed charter. Before starting that work, the author wanted to better understand the historic policy context and created this paper to fill that need.

This study aims to explore how young people in child and adolescent mental health services (CAMHS) in the UK, Ireland, The Netherlands and Croatia, experienced leaving CAMHS and identified a range of factors impeding optimal discharge or transition to adult mental health services (AMHS).

Interviews about discharge or transition planning, including what information was provided about their ongoing mental health needs, undertaken with 34 young people aged 17–24, all previous or current attendees of CAMHS. Some interviews included accounts by parents or carers. Data were thematically analysed.

A number of previously well-documented barriers to a well-delivered discharge or transition were noted. Two issues less frequently reported on were identified and further discussed; they are the provision of an adequately explained, timely and appropriately used diagnosis and post-CAMHS medication management. Overall, planning processes for discharging or transitioning young people from CAMHS are often sub-optimal. Practice with regard to how and when young people are given a diagnosis and arrangements for the continuation of prescribed medication appear to be areas requiring improvement.

Study participants came from a large cohort involving a wide range of different services and health systems in the first pan-European study exploring the CAMHS to adult service interface. Two novel and infrequently discussed issues in the literature about young people’s mental health transitions, diagnosis and medication management were identified in this cohort and worthy of further study.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.

The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle. Based on data mechanism models, it predicts the lifespan of key components, evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.

The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system, which helps operators to monitor the operation of vehicle online, predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.

This system improves the efficiency of rail vehicle operation, scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.