Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA’s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

SUSPENSION FAULT DIAGNOSTICS USING VEHICLE PITCH AND ROLL MODELS

The vehicle suspension system, including springs, dampers and stabilizer bars are critical to vehicle riding and handling experience. Automatic fault detection, isolation and failure prognosis of the suspension system will greatly improve vehicle perceived quality, serviceability and customer experience. In our previous work [1], a static diagnostic approach using a ramp with the known slope is proposed. Even though the method can effectively isolate the suspension system faults to each vehicle corner, it requires additional setups at dealerships. In this work, a passive approach using the vehicle pitch and roll models is presented, which can accurately isolate broken springs, leaking dampers, and broken stabilizer bars. Some enabling conditions are proposed to improve the overall algorithm robustness. The proposed solution is verified using the data collected from a test vehicle.

Aflatoxicosis in dogs

Aflatoxins are toxic, naturally occurring bisfuranocoumarin compounds produced by certain strains of the moulds Aspergillus flavus, A. parasiticus and A. nomius. Aflatoxin metabolites cause hepatotoxicity by reacting with macromolecules (including DNA and proteins) to cause fatty liver or liver necrosis. Most cases involve dog food or, less commonly, ingestion of mouldy bread. Periodic outbreaks are reported in dogs, most recently at the end of 2020 to early 2021 in the US. Multiple dogs may be involved in incidents and the dogs usually present with gastrointestinal signs, lethargy, melaena and jaundice. Diagnosis is based on a history of possible ingestion and laboratory confirmation of aflatoxin(s) in suspect material. In the liver the typical histological changes are centrilobular necrosis of the liver and bile duct proliferation. Treatment of aflatoxicosis in dogs in supportive, with management of liver failure. Prognosis depends on the severity of liver damage, but mortality rates in dogs with aflatoxicosis are high.

Role of Irisin in Myocardial Infarction, Heart Failure, and Cardiac Hypertrophy

Irisin is a myokine derived from the cleavage of fibronectin type III domain-containing 5. Irisin regulates mitochondrial energy, glucose metabolism, fatty acid oxidation, and fat browning. Skeletal muscle and cardiomyocytes produce irisin and affect various cardiovascular functions. In the early phase of acute myocardial infarction, an increasing irisin level can reduce endothelial damage by inhibiting inflammation and oxidative stress. By contrast, higher levels of irisin in the later phase of myocardial infarction are associated with more cardiovascular events. During different stages of heart failure, irisin has various influences on mitochondrial dysfunction, oxidative stress, metabolic imbalance, energy expenditure, and heart failure prognosis. Irisin affects blood pressure and controls hypertension through modulating vasodilatation. Moreover, irisin can enhance vasoconstriction via the hypothalamus. Because of these dual effects of irisin on cardiovascular physiology, irisin can be a critical therapeutic target in cardiovascular diseases. This review focuses on the complex functions of irisin in myocardial ischemia, heart failure, and cardiac hypertrophy.

Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]